AIT590-002|Term Project

2020 U.S. Presidential Election Sentimental Analysis: Trump Vs. Biden Tweets

Team9: Elham Jafarghomi, Saravana Vallaban, HaniehSadat Taghavi

In [1]:
#Import required packages and libraries
import string                                # import string library function  
import re
import nltk
#nltk.download("vader_lexicon")               #only one time to download
from collections import Counter
from nltk.sentiment.vader import SentimentIntensityAnalyzer
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from wordcloud import WordCloud, STOPWORDS
import pandas as pd
import matplotlib.pyplot as plt
import plotly.graph_objects as go
from sklearn.model_selection import train_test_split #importing train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB                     
from sklearn.metrics import classification_report
from sklearn import metrics
from sklearn.naive_bayes import BernoulliNB              
from sklearn import linear_model as lm

#Setting the display to maximum
pd.set_option('display.max_columns', None)
pd.set_option('display.max_colwidth', None)

Section 1| Train Dataset

In [2]:
#read in the training dataset as dataframe using pandas library
traindf = pd.read_csv('C:/Users/ellie/OneDrive/Documents/GMU/AIT590-Natural Language Processing/Project/train.csv', encoding = "ISO-8859-1")
In [3]:
#Get the dimention of the dataset
traindf.shape
Out[3]:
(99982, 3)
In [4]:
#check the number of duplicate rows
traindfrow = traindf[traindf.duplicated()]
print("Duplicate Rows:")
print(traindfrow)
# ==> There is no duplicate
Duplicate Rows:
Empty DataFrame
Columns: [ItemID, Sentiment, SentimentText]
Index: []
In [5]:
#check the number of missing values 
traindf.isnull().sum()
# ==> There is no missing values
Out[5]:
ItemID           0
Sentiment        0
SentimentText    0
dtype: int64
In [6]:
#Display train dataset summary
traindf.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 99982 entries, 0 to 99981
Data columns (total 3 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   ItemID         99982 non-null  int64 
 1   Sentiment      99982 non-null  int64 
 2   SentimentText  99982 non-null  object
dtypes: int64(2), object(1)
memory usage: 2.3+ MB
In [7]:
#Display the first 6 row of the train dataset
traindf.head(6)
Out[7]:
ItemID Sentiment SentimentText
0 1 0 is so sad for my APL friend.............
1 2 0 I missed the New Moon trailer...
2 3 1 omg its already 7:30 :O
3 4 0 .. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on (30mins)...
4 5 0 i think mi bf is cheating on me!!! T_T
5 6 0 or i just worry too much?
In [8]:
#Drop the column that will not be used in this analysis; get the text and matching labels
traindf.drop(['ItemID'], inplace=True, axis=1)
In [9]:
#Decode the binary labels to Positive and Negative sentiment label for the visualization purpose 
decode_map = {0: "Negative", 1: "Positive"}
def sentiment_decode(label):
    return decode_map[int(label)]
traindf.Sentiment = traindf.Sentiment .apply(lambda x: sentiment_decode(x))
traindf.head(6)
Out[9]:
Sentiment SentimentText
0 Negative is so sad for my APL friend.............
1 Negative I missed the New Moon trailer...
2 Positive omg its already 7:30 :O
3 Negative .. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on (30mins)...
4 Negative i think mi bf is cheating on me!!! T_T
5 Negative or i just worry too much?
In [10]:
#Exploratory Analysis; plotting the Sentiment pre-labels distribution
Sentiment_count = Counter(traindf.Sentiment)

plt.figure(figsize=(5,5))
plt.bar(Sentiment_count.keys(), Sentiment_count.values(), color =['red', 'Green'])
plt.title("TrainData Sentiment pre-Labels Distribuition")

#Printing the number of positive and negative labels
print('number of positve tagged sentimentText is:  {}'.format(len(traindf['Sentiment'][traindf.Sentiment == 'Positive'])))
print('number of negative tagged sentimentText is: {}'.format(len(traindf['Sentiment'][traindf.Sentiment == 'Negative'])))
print('total number of rows are: {}'.format(traindf.shape[0]))

#Note ==> Positive and Negatives seem to be fairly distributed
number of positve tagged sentimentText is:  56457
number of negative tagged sentimentText is: 43525
total number of rows are: 99982

Data Preprocessing

In our data cleaning process we found that it works much better to first tokenize the data before text cleaning in our case.

In [11]:
#Function to tokenize the text column
def tokenize_text(textdata):
    textdata= textdata.lower()
    textdata = word_tokenize(textdata)
    return textdata
traindf['tokens'] = traindf['SentimentText'].apply(lambda x: tokenize_text(x.lower()))
traindf.head()
Out[11]:
Sentiment SentimentText tokens
0 Negative is so sad for my APL friend............. [is, so, sad, for, my, apl, friend, .............]
1 Negative I missed the New Moon trailer... [i, missed, the, new, moon, trailer, ...]
2 Positive omg its already 7:30 :O [omg, its, already, 7:30, :, o]
3 Negative .. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on (30mins)... [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, been, at, this, dentist, since, 11, .., i, was, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...]
4 Negative i think mi bf is cheating on me!!! T_T [i, think, mi, bf, is, cheating, on, me, !, !, !, t_t]
In [12]:
#Function to lemmatize the words and get the root of words
from nltk.stem import WordNetLemmatizer
lemmatizer = WordNetLemmatizer()

def lemma(textdata):
    textdata = [lemmatizer.lemmatize(token, pos="v") for token in textdata]
    return textdata

traindf['lemma_tokens'] = traindf['tokens'].apply(lambda x: lemma(x))
traindf.head(6)
Out[12]:
Sentiment SentimentText tokens lemma_tokens
0 Negative is so sad for my APL friend............. [is, so, sad, for, my, apl, friend, .............] [be, so, sad, for, my, apl, friend, .............]
1 Negative I missed the New Moon trailer... [i, missed, the, new, moon, trailer, ...] [i, miss, the, new, moon, trailer, ...]
2 Positive omg its already 7:30 :O [omg, its, already, 7:30, :, o] [omg, its, already, 7:30, :, o]
3 Negative .. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on (30mins)... [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, been, at, this, dentist, since, 11, .., i, was, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, be, at, this, dentist, since, 11, .., i, be, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...]
4 Negative i think mi bf is cheating on me!!! T_T [i, think, mi, bf, is, cheating, on, me, !, !, !, t_t] [i, think, mi, bf, be, cheat, on, me, !, !, !, t_t]
5 Negative or i just worry too much? [or, i, just, worry, too, much, ?] [or, i, just, worry, too, much, ?]
In [13]:
#Removing stopwords using spacy; stopwords are customized such that some words that are not written correctly such as mi instead 
#of my and etc, be added to stopwords for rwmoval and some words are excluded since the number of spacy stopwordfs are 
#double of the nltk

import spacy
from spacy.lang.en.stop_words import STOP_WORDS
STOP_WORDS |= {"mi", "gon", "na", "cause", "atleast", "wiiit", "doin"}
STOP_WORDS -= {"less", "serious", "against", "enough", "never", "few", "more", "most", "mostly", "together", "very", 
              "many", "former", "say", "behind", "again", "beyond", "least"}
def remove_stopwords(textdata):
    textdata = [token for token in textdata if token not in STOP_WORDS]
    return textdata

traindf['nostop_tokens'] = traindf['lemma_tokens'].apply(lambda x: remove_stopwords(x))
traindf.head(6)
Out[13]:
Sentiment SentimentText tokens lemma_tokens nostop_tokens
0 Negative is so sad for my APL friend............. [is, so, sad, for, my, apl, friend, .............] [be, so, sad, for, my, apl, friend, .............] [sad, apl, friend, .............]
1 Negative I missed the New Moon trailer... [i, missed, the, new, moon, trailer, ...] [i, miss, the, new, moon, trailer, ...] [miss, new, moon, trailer, ...]
2 Positive omg its already 7:30 :O [omg, its, already, 7:30, :, o] [omg, its, already, 7:30, :, o] [omg, 7:30, :, o]
3 Negative .. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on (30mins)... [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, been, at, this, dentist, since, 11, .., i, was, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, be, at, this, dentist, since, 11, .., i, be, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., dentist, 11, .., suposed, 2, crown, (, 30mins, ), ...]
4 Negative i think mi bf is cheating on me!!! T_T [i, think, mi, bf, is, cheating, on, me, !, !, !, t_t] [i, think, mi, bf, be, cheat, on, me, !, !, !, t_t] [think, bf, cheat, !, !, !, t_t]
5 Negative or i just worry too much? [or, i, just, worry, too, much, ?] [or, i, just, worry, too, much, ?] [worry, ?]
In [14]:
#Create a new column to store text after stopwords removal and lemmatization
traindf['nostop_string'] = [' '.join(map(str, lists)) for lists in traindf['nostop_tokens']]
traindf.head()
Out[14]:
Sentiment SentimentText tokens lemma_tokens nostop_tokens nostop_string
0 Negative is so sad for my APL friend............. [is, so, sad, for, my, apl, friend, .............] [be, so, sad, for, my, apl, friend, .............] [sad, apl, friend, .............] sad apl friend .............
1 Negative I missed the New Moon trailer... [i, missed, the, new, moon, trailer, ...] [i, miss, the, new, moon, trailer, ...] [miss, new, moon, trailer, ...] miss new moon trailer ...
2 Positive omg its already 7:30 :O [omg, its, already, 7:30, :, o] [omg, its, already, 7:30, :, o] [omg, 7:30, :, o] omg 7:30 : o
3 Negative .. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on (30mins)... [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, been, at, this, dentist, since, 11, .., i, was, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, be, at, this, dentist, since, 11, .., i, be, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., dentist, 11, .., suposed, 2, crown, (, 30mins, ), ...] .. omgaga . im sooo im gunna cry . dentist 11 .. suposed 2 crown ( 30mins ) ...
4 Negative i think mi bf is cheating on me!!! T_T [i, think, mi, bf, is, cheating, on, me, !, !, !, t_t] [i, think, mi, bf, be, cheat, on, me, !, !, !, t_t] [think, bf, cheat, !, !, !, t_t] think bf cheat ! ! ! t_t

Punctuation Removal

In [15]:
#Storing the sets of punctuation in variable result  
punctuations = string.punctuation     
#Printing all punctuation 
print(punctuations)  
!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~
In [16]:
#Refrence https://www.kaggle.com/ragnisah/text-data-cleaning-tweets-analysis
#Function for removing punctuations, http, mentions and retweets is defined
def remove_punctuation(textdata):
   
    textdata = re.sub("(@[A-Za-z0-9]+)"," ", textdata)     #removing all user mentions 
    textdata = re.sub("([^0-9A-Za-z \t])"," ", textdata)
    textdata =re.sub(r'’https?:\/\/(www\.)?[-a-zA-Z0–9@:%._\+~#=]{2,256}\.[a-z]{2,6}\b([-a-zA-Z0–9@:%_\+.~#?&//=]*)', " ", textdata)
    textdata = re.sub('[0-9]+', '', textdata)     #removing all digits 
    textdata = re.sub(r'^rt[\s]+','', textdata)  # removes RT
    textdata = textdata.lower()
    textdata = "".join([char for char in textdata if char not in punctuations])

    return textdata
traindf['cleanedText'] = traindf['nostop_string'].apply(lambda x: remove_punctuation(x))
traindf.head(10)
Out[16]:
Sentiment SentimentText tokens lemma_tokens nostop_tokens nostop_string cleanedText
0 Negative is so sad for my APL friend............. [is, so, sad, for, my, apl, friend, .............] [be, so, sad, for, my, apl, friend, .............] [sad, apl, friend, .............] sad apl friend ............. sad apl friend
1 Negative I missed the New Moon trailer... [i, missed, the, new, moon, trailer, ...] [i, miss, the, new, moon, trailer, ...] [miss, new, moon, trailer, ...] miss new moon trailer ... miss new moon trailer
2 Positive omg its already 7:30 :O [omg, its, already, 7:30, :, o] [omg, its, already, 7:30, :, o] [omg, 7:30, :, o] omg 7:30 : o omg o
3 Negative .. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on (30mins)... [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, been, at, this, dentist, since, 11, .., i, was, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, be, at, this, dentist, since, 11, .., i, be, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., dentist, 11, .., suposed, 2, crown, (, 30mins, ), ...] .. omgaga . im sooo im gunna cry . dentist 11 .. suposed 2 crown ( 30mins ) ... omgaga im sooo im gunna cry dentist suposed crown mins
4 Negative i think mi bf is cheating on me!!! T_T [i, think, mi, bf, is, cheating, on, me, !, !, !, t_t] [i, think, mi, bf, be, cheat, on, me, !, !, !, t_t] [think, bf, cheat, !, !, !, t_t] think bf cheat ! ! ! t_t think bf cheat t t
5 Negative or i just worry too much? [or, i, just, worry, too, much, ?] [or, i, just, worry, too, much, ?] [worry, ?] worry ? worry
6 Positive Juuuuuuuuuuuuuuuuussssst Chillin!! [juuuuuuuuuuuuuuuuussssst, chillin, !, !] [juuuuuuuuuuuuuuuuussssst, chillin, !, !] [juuuuuuuuuuuuuuuuussssst, chillin, !, !] juuuuuuuuuuuuuuuuussssst chillin ! ! juuuuuuuuuuuuuuuuussssst chillin
7 Negative Sunny Again Work Tomorrow :-| TV Tonight [sunny, again, work, tomorrow, :, -|, tv, tonight] [sunny, again, work, tomorrow, :, -|, tv, tonight] [sunny, again, work, tomorrow, :, -|, tv, tonight] sunny again work tomorrow : -| tv tonight sunny again work tomorrow tv tonight
8 Positive handed in my uniform today . i miss you already [handed, in, my, uniform, today, ., i, miss, you, already] [hand, in, my, uniform, today, ., i, miss, you, already] [hand, uniform, today, ., miss] hand uniform today . miss hand uniform today miss
9 Positive hmmmm.... i wonder how she my number @-) [hmmmm, ...., i, wonder, how, she, my, number, @, -, )] [hmmmm, ...., i, wonder, how, she, my, number, @, -, )] [hmmmm, ...., wonder, number, @, -, )] hmmmm .... wonder number @ - ) hmmmm wonder number
In [17]:
#Short words such as tt, or and etc removal, since does not have any contribution  
traindf['cleanedText'] = traindf['cleanedText'].apply(lambda x: ' '.join([w for w in x.split() if len(w) > 2]))
traindf.head(6)
Out[17]:
Sentiment SentimentText tokens lemma_tokens nostop_tokens nostop_string cleanedText
0 Negative is so sad for my APL friend............. [is, so, sad, for, my, apl, friend, .............] [be, so, sad, for, my, apl, friend, .............] [sad, apl, friend, .............] sad apl friend ............. sad apl friend
1 Negative I missed the New Moon trailer... [i, missed, the, new, moon, trailer, ...] [i, miss, the, new, moon, trailer, ...] [miss, new, moon, trailer, ...] miss new moon trailer ... miss new moon trailer
2 Positive omg its already 7:30 :O [omg, its, already, 7:30, :, o] [omg, its, already, 7:30, :, o] [omg, 7:30, :, o] omg 7:30 : o omg
3 Negative .. Omgaga. Im sooo im gunna CRy. I've been at this dentist since 11.. I was suposed 2 just get a crown put on (30mins)... [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, been, at, this, dentist, since, 11, .., i, was, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., i, 've, be, at, this, dentist, since, 11, .., i, be, suposed, 2, just, get, a, crown, put, on, (, 30mins, ), ...] [.., omgaga, ., im, sooo, im, gunna, cry, ., dentist, 11, .., suposed, 2, crown, (, 30mins, ), ...] .. omgaga . im sooo im gunna cry . dentist 11 .. suposed 2 crown ( 30mins ) ... omgaga sooo gunna cry dentist suposed crown mins
4 Negative i think mi bf is cheating on me!!! T_T [i, think, mi, bf, is, cheating, on, me, !, !, !, t_t] [i, think, mi, bf, be, cheat, on, me, !, !, !, t_t] [think, bf, cheat, !, !, !, t_t] think bf cheat ! ! ! t_t think cheat
5 Negative or i just worry too much? [or, i, just, worry, too, much, ?] [or, i, just, worry, too, much, ?] [worry, ?] worry ? worry
In [18]:
#We use the tokenize function built earlier to tokenize clean text column after all data preprocessing and cleaning
traindf['cleanTweets'] = traindf['cleanedText'].apply(lambda x: tokenize_text(x.lower()))
In [19]:
#Dropping the extra columns created during data preprocessing and cleaning
columntodrop = ['SentimentText', 'tokens','lemma_tokens', 'nostop_tokens', 'nostop_string']
traindf.drop(columntodrop, inplace=True, axis=1)
traindf.head(6)
Out[19]:
Sentiment cleanedText cleanTweets
0 Negative sad apl friend [sad, apl, friend]
1 Negative miss new moon trailer [miss, new, moon, trailer]
2 Positive omg [omg]
3 Negative omgaga sooo gunna cry dentist suposed crown mins [omgaga, sooo, gunna, cry, dentist, suposed, crown, mins]
4 Negative think cheat [think, cheat]
5 Negative worry [worry]
In [20]:
#Getting the length of tokens in each tokenized tweet (after removing stopwords)
#convert column of lists to string
cleanTweets_len = []
for i in range(len(traindf['cleanTweets'])):
    cleanTweets_len.append(len(traindf['cleanTweets'][i]))

traindf['cleanTweets_count'] = cleanTweets_len
traindf.head(6)
Out[20]:
Sentiment cleanedText cleanTweets cleanTweets_count
0 Negative sad apl friend [sad, apl, friend] 3
1 Negative miss new moon trailer [miss, new, moon, trailer] 4
2 Positive omg [omg] 1
3 Negative omgaga sooo gunna cry dentist suposed crown mins [omgaga, sooo, gunna, cry, dentist, suposed, crown, mins] 8
4 Negative think cheat [think, cheat] 2
5 Negative worry [worry] 1
In [21]:
#Check to see if we have at least one token left after preprocessing and only keep those rows
traindf= traindf[traindf["cleanTweets_count"]>0]
In [22]:
#Resetting indexes after all cleaning
traindf.reset_index(inplace=True, drop=True)
In [23]:
#Get the dimention of the dataset;95 rows had no words remained after cleaning and they were only stopwords--not contributing
traindf.shape
Out[23]:
(99887, 4)

Word Cloud of positive and negative sentiments

In [24]:
#Creating dataframe for Positive and negative wordstems
Positive_sents= traindf[traindf['Sentiment']== "Positive"]
Negative_sents= traindf[traindf['Sentiment']== "Negative"]
In [25]:
#creating the list of positive and negative words for visualizations
positive_tokens = Positive_sents['cleanTweets'].tolist()
positive_tokens = [item for sublist in positive_tokens for item in sublist]

negative_tokens = Negative_sents['cleanTweets'].tolist()
negative_tokens = [item for sublist in negative_tokens for item in sublist]
In [26]:
#creating wordcloud to visualize positive words in traindata
pos_wordcloud = WordCloud(max_font_size=100, max_words=400,scale=10,relative_scaling=.6,background_color="black", colormap = "rainbow").generate(', '.join(positive_tokens))

# Display the generated image:
plt.figure(figsize=(12,10))
plt.imshow(pos_wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()
In [27]:
#creating wordcloud to visualize negative words in traindata
neg_wordcloud = WordCloud(max_font_size=100, max_words=400,scale=10,relative_scaling=.6,background_color="black", colormap = "rainbow").generate(', '.join(negative_tokens))

# Display the generated image:
plt.figure(figsize=(12,10))
plt.imshow(neg_wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

Sentiment Analysis Using Vader

In [28]:
#Renameing the SentimentIntensityAnalyzer to vader
vader = SentimentIntensityAnalyzer()
In [29]:
#Here we try a baseline solution accuray on the training dataset 
#A copy of the train data is created
traindf_baseline = traindf.copy(deep=True)
In [30]:
traindf_baseline['Sentiment scores'] = traindf_baseline['cleanedText'].apply(lambda cleanedText: vader.polarity_scores(cleanedText))
traindf_baseline.head(6)
Out[30]:
Sentiment cleanedText cleanTweets cleanTweets_count Sentiment scores
0 Negative sad apl friend [sad, apl, friend] 3 {'neg': 0.425, 'neu': 0.137, 'pos': 0.438, 'compound': 0.0258}
1 Negative miss new moon trailer [miss, new, moon, trailer] 4 {'neg': 0.348, 'neu': 0.652, 'pos': 0.0, 'compound': -0.1531}
2 Positive omg [omg] 1 {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0}
3 Negative omgaga sooo gunna cry dentist suposed crown mins [omgaga, sooo, gunna, cry, dentist, suposed, crown, mins] 8 {'neg': 0.307, 'neu': 0.693, 'pos': 0.0, 'compound': -0.4767}
4 Negative think cheat [think, cheat] 2 {'neg': 0.75, 'neu': 0.25, 'pos': 0.0, 'compound': -0.4588}
5 Negative worry [worry] 1 {'neg': 1.0, 'neu': 0.0, 'pos': 0.0, 'compound': -0.4404}
In [31]:
#Create a column for the compound scores 
traindf_baseline['compound'] = traindf_baseline['Sentiment scores'].apply(lambda score_dict: score_dict['compound'])
traindf_baseline.head(2)
Out[31]:
Sentiment cleanedText cleanTweets cleanTweets_count Sentiment scores compound
0 Negative sad apl friend [sad, apl, friend] 3 {'neg': 0.425, 'neu': 0.137, 'pos': 0.438, 'compound': 0.0258} 0.0258
1 Negative miss new moon trailer [miss, new, moon, trailer] 4 {'neg': 0.348, 'neu': 0.652, 'pos': 0.0, 'compound': -0.1531} -0.1531
In [32]:
#Decide sentiment as positive, negative and neutral
traindf_baseline['vaderSentiment'] = traindf_baseline['compound'].apply(lambda compound: 'Positive' if compound> 0 else ('Negative' if
                                                      compound<0 else 'Neutral'))
traindf_baseline.head(6)
Out[32]:
Sentiment cleanedText cleanTweets cleanTweets_count Sentiment scores compound vaderSentiment
0 Negative sad apl friend [sad, apl, friend] 3 {'neg': 0.425, 'neu': 0.137, 'pos': 0.438, 'compound': 0.0258} 0.0258 Positive
1 Negative miss new moon trailer [miss, new, moon, trailer] 4 {'neg': 0.348, 'neu': 0.652, 'pos': 0.0, 'compound': -0.1531} -0.1531 Negative
2 Positive omg [omg] 1 {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound': 0.0} 0.0000 Neutral
3 Negative omgaga sooo gunna cry dentist suposed crown mins [omgaga, sooo, gunna, cry, dentist, suposed, crown, mins] 8 {'neg': 0.307, 'neu': 0.693, 'pos': 0.0, 'compound': -0.4767} -0.4767 Negative
4 Negative think cheat [think, cheat] 2 {'neg': 0.75, 'neu': 0.25, 'pos': 0.0, 'compound': -0.4588} -0.4588 Negative
5 Negative worry [worry] 1 {'neg': 1.0, 'neu': 0.0, 'pos': 0.0, 'compound': -0.4404} -0.4404 Negative
In [33]:
#Drop the neutral sentiments (compound scores of 0)
traindf_baseline= traindf_baseline[traindf_baseline.vaderSentiment!='Neutral']
In [34]:
from sklearn.metrics import accuracy_score
#Checking the vaderSentiment prediction with sentiment of the data (pre-labeled); the level of accuracy
print("Accuracy resulted from baseline solution is:")
round((accuracy_score(traindf_baseline['Sentiment'], traindf_baseline['vaderSentiment'])*100),2)
Accuracy resulted from baseline solution is:
Out[34]:
69.7

Model training Process

In [35]:
#Convert the cleanTweet tokens to clean text for being used in vectorization
traindf['cleanTweets'] = [','.join(map(str, lists)) for lists in traindf['cleanTweets']]
In [36]:
#Convert the sentiments to binary for using in model
traindf['Sentiment_binary'] = traindf['Sentiment'].map({'Positive':1, 'Negative':0})
In [37]:
#Drop the sentiment column with string positive and negrative names
traindf = traindf.drop("Sentiment", axis = 1)
In [38]:
#TF-IDF Vectorization
#Because our traindata and test data are two different datasets, and number of features are not the same, we use maximum 
#number of features that we can use in order to be able to run our model on our test datasets
tfidfvec = TfidfVectorizer(max_features = 2000, ngram_range=(1,2)).fit(traindf.cleanTweets)

# create matrix from the vectorizer
V = tfidfvec.transform(traindf.cleanTweets)

# create the transformed df
transformed_df = pd.DataFrame(V.toarray(), columns=tfidfvec.get_feature_names())
d = traindf.join(transformed_df)
d.head()
Out[38]:
cleanedText cleanTweets cleanTweets_count Sentiment_binary aaron able absolutely abt accept access accident account ace ache act action actual actually adam add add train addict address admit adorable adrienne advice afford afraid afternoon again against age agent ago agree aha ahaha ahead ahh ahhh aim aint air airport aka alan alancarr album alex alexalltimelow alexandramusic ali alice alive all allow ally alot alright alyankovic alyssa alyssa milano amanda amanda holden amandabynes amandapalmer amaze amber amber benson america american amy ana and andrea andrew andy andyclemmensen ang angel angela angry animals anime ann anna anne annoy anoopdoggdesai answer anthony anymore anytime anyways anz anz rocks apart aplusk app apparently appear apple apply appreciate apps april area arent argh arm arrive art article asap ash ashley ashleyltmsyf ashleytisdale ask asleep asos asot ass assume aswell atl atm attack attempt attend attention aubreyoday audio august aussie austin australia available avatar avoid awake award away awe awesome awesome thank awful awh awhile aww aww sorry aww thank awww awwww awwwww babe babes baby babygirlparis background backstreetboys bad bad day bad time badly bag bailon bake ball banana band bang bank banksyart bar barely base baseball basically bass bath battery bay bbc bbq bday beach bean bear beat beautiful beauty becky bed bee beer begin behind believe bell bella ben benson best best friend bet beta beth better better soon bgt big bigger biggest bike bill billy billyraycyrus bing bio bird birthday bit bitch bite black blackberry blah blame blast bless blink blip block blog blokeslib blonde blood bloody blow blue board boat bob bobbyllew body bone boo book boom booo boot bore borrow boss boston bother bottle bout bowl bowwow box boy boy kill boyfriend boys brad bradie bradiewebbstack brain brand brandon brazil bread break break heart breakfast breathe brian bright brilliant bring british britney britneyspears bro brooke brother brothers brown bsb btw buckhollywood bud buddy bug build bum bummer bunch bunny burn bus business busy butt butter button buy bye cable cake cali california cam camera camp canada cancel cancer candy cant cant wait cap capplegate car card care careful carry case cash cat catch cathy cause cavs celebrate cell center certain certainly chad challenge chance change channel char character charge charlie chase chat cheap check cheer cheese chelsea cherry chi chicago chick chicken child children chill chillin chinese chip chocolate choice choose chris chris daughtry chrisdjmoyles chuck church city claire class classic clean clear clearly click client clip clock close closer clothe cloud club clue coast cod code coffee cold coldplay college color colorblindfish colour com com add come come home come soon come visit comeagainjen comedyqueen comment common community comp company compare complain complete completely compliment computer con concern concert conference confirm confuse congrats 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In [39]:
#Drop the columns that are not useful for the model
d = d.drop(["cleanedText","cleanTweets","cleanTweets_count"], axis = 1)
d.head(4)
Out[39]:
Sentiment_binary aaron able absolutely abt accept access accident account ace ache act action actual actually adam add add train addict address admit adorable adrienne advice afford afraid afternoon again against age agent ago agree aha ahaha ahead ahh ahhh aim aint air airport aka alan alancarr album alex alexalltimelow alexandramusic ali alice alive all allow ally alot alright alyankovic alyssa alyssa milano amanda amanda holden amandabynes amandapalmer amaze amber amber benson america american amy ana and andrea andrew andy andyclemmensen ang angel angela angry animals anime ann anna anne annoy anoopdoggdesai answer anthony anymore anytime anyways anz anz rocks apart aplusk app apparently appear apple apply appreciate apps april area arent argh arm arrive art article asap ash ashley ashleyltmsyf ashleytisdale ask asleep asos asot ass assume aswell atl atm attack attempt attend attention aubreyoday audio august aussie austin australia available avatar avoid awake award away awe awesome awesome thank awful awh awhile aww aww sorry aww thank awww awwww awwwww babe babes baby babygirlparis background backstreetboys bad bad day bad time badly bag bailon bake ball banana band bang bank banksyart bar barely base baseball basically bass bath battery bay bbc bbq bday beach bean bear beat beautiful beauty becky bed bee beer begin behind believe bell bella ben benson best best friend bet beta beth better better soon bgt big bigger biggest bike bill billy billyraycyrus bing bio bird birthday bit bitch bite black blackberry blah blame blast bless blink blip block blog blokeslib blonde blood bloody blow blue board boat bob bobbyllew body bone boo book boom booo boot bore borrow boss boston bother bottle bout bowl bowwow box boy boy kill boyfriend boys brad bradie bradiewebbstack brain brand brandon brazil bread break break heart breakfast breathe brian bright brilliant bring british britney britneyspears bro brooke brother brothers brown bsb btw buckhollywood bud buddy 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spell spend spirit spoil sport spot spread spring squarespace sry stage stalk stand star star trek starbucks start state station status stay steal step stick stock stoke stomach stop store stories storm story straight strange stream street stress strong student studio study stuff stupid style success suck suffer sugar suggest suit sum summer sun sunday sunny sunshine super supernatural support suppose sure surgery surprise survive susan sushi sux swear sweet sweet dream sweetheart sweetie swim swine swine flu switch sydney system table tag talent talented talk tan taste tattoo tax taylor tea teach teacher team tear tease tech teeth tell tempt term terrible test texas text tha thank thank follow thank followfriday thank god thank good thank guy thank hope thank lot thank love thank share thank think thank very thankyou thanx thats thats good the theme theres thing things think think good think like think need thnx tho thoughts throat throw tht thumb thunder thurs thursday thx ticket tie tight til till time tiny tinyurl tinyurl com tip tire title tix today together tom tomorrow tomorrow night tonight tonite tons tony tool tooo topic topics toronto total totally totally agree touch tough tour town toy track trade traffic trailer train train pay travel treat tree trek trend trick trip trouble truck true truly trust truth try try again tryin tuesday tumblr tummy tune turn tweeps tweet tweetdeck tweeteradder tweeteradder com tweeterfollow tweeterfollow com tweetie twice twilight twin twit twitpic twitpic com twitter txt type ugh ugly umm ummm uncle understand unfortunately uni update upgrade upload upset use use twitter use www user usual usually vacation vegas version very very cool very good very happy very nice very sad very very vid video videos vids view vip virtual visit voice vote wait wait hear wait till wake walk wall wan want want come want know want more want say want watch war warm warn wash wasnt waste wat watch watchin water wave way way home ways wear weather web website wed wednesday wee week weekend weeks weight weird welcome welcome twitter wen west wet whats white wide wife win wind window windows wine winter wish wish come wit woman women wonder wonderful wont wont let woo woot word work work again work day work today work tomorrow work work workin world worry worse worst worth wouldnt wow write wrong wtf wud www www tweeteradder www tweeterfollow xbox xoxo xxx xxxx yah yall yawn yay yea yeah yeah good yeah know yeah think yeahh year year old years years ago yeh yep yes yes yes yesterday york you young youre youtube youu yrs yum yummy yup
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Train the Logistic Regression Model

In [40]:
#logistic regression classifier
y = d.Sentiment_binary
X = d.drop('Sentiment_binary', axis=1)

#split the data to train and test for predicting sentiment of text
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, random_state=123)

#Call the logistic regression model for training 
log_reg = LogisticRegression(max_iter=4000).fit(X_train, y_train)

#predict the sentiment on the text data
y_pred = log_reg.predict(X_test)

#Print the accuracy resulted from logistic regression model
accuracy_score_lr = metrics.accuracy_score(y_pred,y_test)
print("Accuracy resulted from Logistic Regression model is:")
print(str('{:04.2f}'.format(accuracy_score_lr*100))+ '%')


#Print the confusion Matrix for the model
cfmatrix_lr = confusion_matrix(y_test, y_pred)
print('Confusion Matrix for the Logistic Regression model\n')
print(cfmatrix_lr)

#Print the classification report including the Precision, Recall and F1 Score 
print(classification_report(y_test, y_pred))
Accuracy resulted from Logistic Regression model is:
73.39%
Confusion Matrix for the Logistic Regression model

[[5292 3364]
 [1953 9369]]
              precision    recall  f1-score   support

           0       0.73      0.61      0.67      8656
           1       0.74      0.83      0.78     11322

    accuracy                           0.73     19978
   macro avg       0.73      0.72      0.72     19978
weighted avg       0.73      0.73      0.73     19978

In [41]:
#Plot the confusion matrix
cfmatrix_lr = confusion_matrix(y_true=y_test, y_pred=y_pred)

# Print the confusion matrix using Matplotlib

fig, ax = plt.subplots(figsize=(5, 5))
ax.matshow(cfmatrix_lr, cmap=plt.cm.Oranges, alpha=0.3)
for i in range(cfmatrix_lr.shape[0]):
    for j in range(cfmatrix_lr.shape[1]):
        ax.text(x=j, y=i,s=cfmatrix_lr[i, j], va='center', ha='center', size='xx-large')
 
plt.xlabel('Predictions', fontsize=18)
plt.ylabel('Actuals', fontsize=18)
plt.title('Confusion Matrix', fontsize=18)
plt.show()

Train the Multinomial Naive Bayes Model

In [42]:
#split the data to train and test for predicting sentiment of text
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.20, random_state =123)

#Defining the model; will filt Multinominal NB model
model = MultinomialNB()
model.fit(X_train, y_train)

model.score(X_test, y_test)
#predict on test data
y_predict = model.predict(X_test)

##Print the accuracy resulted from Multinomial NB model
accuracy_score = metrics.accuracy_score(y_predict,y_test)
print("Accuracy resulted from Naive Bayes classifier is:")
print(str('{:04.2f}'.format(accuracy_score*100))+ '%')

#Print the classification report including the Precision, Recall and F1 Score 
print(classification_report(y_test, y_predict))
Accuracy resulted from Naive Bayes classifier is:
72.89%
              precision    recall  f1-score   support

           0       0.73      0.59      0.66      8656
           1       0.73      0.83      0.78     11322

    accuracy                           0.73     19978
   macro avg       0.73      0.71      0.72     19978
weighted avg       0.73      0.73      0.72     19978

Train the Bernoulli Naive Bayes Model

In [43]:
#split the data to train and test for predicting sentiment of text
X_trainb, X_testb, y_trainb, y_testb = train_test_split(X, y, test_size =0.20, random_state =123)

#Calling the bernoulli model 
Bmodel = BernoulliNB().fit(X_trainb,y_trainb)

#Predict on test data
yb_predict = Bmodel.predict(X_testb)

#Print the accuracy resulted from Bernoulli NB model
accuracy_score_b = metrics.accuracy_score(yb_predict,y_testb)
print("Accuracy resulted from  Bernoulli Naive Bayes classifier is:")
print(str('{:04.2f}'.format(accuracy_score_b*100))+ '%')

#Print the classification report including the Precision, Recall and F1 Score 
print(classification_report(y_testb, yb_predict))
Accuracy resulted from  Bernoulli Naive Bayes classifier is:
72.70%
              precision    recall  f1-score   support

           0       0.70      0.65      0.67      8656
           1       0.75      0.78      0.77     11322

    accuracy                           0.73     19978
   macro avg       0.72      0.72      0.72     19978
weighted avg       0.73      0.73      0.73     19978

Section 2| Test Datasets

In this section, we will read Donald Trump and Joe Biden tweets dataset that were scrapped from their twitter accounts, and will do all cleaning steps that we did for the general train data above. We then the sentiment analysis using our best performing model that we trained on a general pre-labeled tweet dataset.

Donald Trump Tweets data

In [44]:
#Read in the trump dataset as dataframe 
trumpdf = pd.read_csv('C:/Users/ellie/OneDrive/Documents/GMU/AIT590-Natural Language Processing/Project/Trump.csv', encoding = "ISO-8859-1")
trumpdf.head(6)
Out[44]:
Date Tweet
0 11/2/2020 Just landed in Traverse City, Michigan. Big crowd!
1 11/2/2020 As Christians throughout this great Country celebrate All Souls Day, let's remember those who went before us and built this great nation. May their legacy inspire us as we keep our nation what it has always been: blessed and great!
2 11/2/2020 Joe Biden is promising to delay the vaccine and turn America into a prison state-locking you in your home while letting far-left rioters roam free. The Biden Lockdown will mean no school, no graduations, no weddings, no Thanksgiving, no Christmas, no Fourth of July, and...
3 11/2/2020 Joe Biden is a globalist who spent 47 years outsourcing your jobs, opening your borders, and sacrificing American blood and treasure in endless foreign wars. He shuttered your steel mills, annihilated your coal jobs, and supported every disastrous trade deal for half a century...
4 11/2/2020 I gave Maine everything that Obama/Biden took away from it. 5000 square miles, Lobster, Fishing, ended tariffs from China and E.U. and much more. Vote Trump Maine!
5 11/2/2020 Landing in Scranton, Pennsylvania!
In [45]:
##Get the dimention of trump tweets dataset
trumpdf.shape
Out[45]:
(2570, 2)
In [46]:
#Drop the dupicates 
trumpdf=trumpdf.drop_duplicates()
In [47]:
#Reset indexes after dropping duplicates
trumpdf.reset_index(inplace=True, drop=True)
In [48]:
#Check the dimention after dropping duplicates
trumpdf.shape
Out[48]:
(1411, 2)
In [49]:
#check the number of missing values 
trumpdf.isnull().sum()
# ==> There is no missing values
Out[49]:
Date     0
Tweet    0
dtype: int64
In [50]:
#Tokenize the tweets using the tokenize function defined earlier in the code in section 1
trumpdf['tokens'] = trumpdf['Tweet'].apply(lambda x: tokenize_text(x.lower()))
In [51]:
#Lemmatize tweets ussing the pre-defined function for lemmatizing the tokens in section 1
trumpdf['lemma_tokens'] =trumpdf['tokens'].apply(lambda x: lemma(x))
In [52]:
#Remove stopwords using the pre-defined function for removing stopwords in section 1
trumpdf['nostop_tokens'] = trumpdf['lemma_tokens'].apply(lambda x: remove_stopwords(x))
In [53]:
#Put back the nostop_tokens to sentence format for enabling to use punctuation removal later
trumpdf['nostop_string'] = [' '.join(map(str, lists)) for lists in trumpdf['nostop_tokens']]
In [54]:
#Remove all punctuations, mentions, url links and hashtags using the function defined earlier in section 1
trumpdf['cleanedText'] = trumpdf['nostop_string'].apply(lambda x: remove_punctuation(x))
In [55]:
#Remove all short words that has no meaning and does contribute  
trumpdf['cleanedText'] = trumpdf['cleanedText'].apply(lambda x: ' '.join([w for w in x.split() if len(w) > 2]))
In [56]:
#Tokenize the tweets after all above cleaning process 
trumpdf['cleanTweets'] = trumpdf['cleanedText'].apply(lambda x: tokenize_text(x.lower()))
trumpdf.head(6)
Out[56]:
Date Tweet tokens lemma_tokens nostop_tokens nostop_string cleanedText cleanTweets
0 11/2/2020 Just landed in Traverse City, Michigan. Big crowd! [just, landed, in, traverse, city, ,, michigan, ., big, crowd, !] [just, land, in, traverse, city, ,, michigan, ., big, crowd, !] [land, traverse, city, ,, michigan, ., big, crowd, !] land traverse city , michigan . big crowd ! land traverse city michigan big crowd [land, traverse, city, michigan, big, crowd]
1 11/2/2020 As Christians throughout this great Country celebrate All Souls Day, let's remember those who went before us and built this great nation. May their legacy inspire us as we keep our nation what it has always been: blessed and great! [as, christians, throughout, this, great, country, celebrate, all, souls, day, ,, let, 's, remember, those, who, went, before, us, and, built, this, great, nation, ., may, their, legacy, inspire, us, as, we, keep, our, nation, what, it, has, always, been, :, blessed, and, great, !] [as, christians, throughout, this, great, country, celebrate, all, souls, day, ,, let, 's, remember, those, who, go, before, us, and, build, this, great, nation, ., may, their, legacy, inspire, us, as, we, keep, our, nation, what, it, have, always, be, :, bless, and, great, !] [christians, great, country, celebrate, souls, day, ,, let, remember, build, great, nation, ., legacy, inspire, nation, :, bless, great, !] christians great country celebrate souls day , let remember build great nation . legacy inspire nation : bless great ! christians great country celebrate souls day let remember build great nation legacy inspire nation bless great [christians, great, country, celebrate, souls, day, let, remember, build, great, nation, legacy, inspire, nation, bless, great]
2 11/2/2020 Joe Biden is promising to delay the vaccine and turn America into a prison state-locking you in your home while letting far-left rioters roam free. The Biden Lockdown will mean no school, no graduations, no weddings, no Thanksgiving, no Christmas, no Fourth of July, and... [joe, biden, is, promising, to, delay, the, vaccine, and, turn, america, into, a, prison, state-locking, you, in, your, home, while, letting, far-left, rioters, roam, free, ., the, biden, lockdown, will, mean, no, school, ,, no, graduations, ,, no, weddings, ,, no, thanksgiving, ,, no, christmas, ,, no, fourth, of, july, ,, and, ...] [joe, biden, be, promise, to, delay, the, vaccine, and, turn, america, into, a, prison, state-locking, you, in, your, home, while, let, far-left, rioters, roam, free, ., the, biden, lockdown, will, mean, no, school, ,, no, graduations, ,, no, weddings, ,, no, thanksgiving, ,, no, christmas, ,, no, fourth, of, july, ,, and, ...] [joe, biden, promise, delay, vaccine, turn, america, prison, state-locking, home, let, far-left, rioters, roam, free, ., biden, lockdown, mean, school, ,, graduations, ,, weddings, ,, thanksgiving, ,, christmas, ,, fourth, july, ,, ...] joe biden promise delay vaccine turn america prison state-locking home let far-left rioters roam free . biden lockdown mean school , graduations , weddings , thanksgiving , christmas , fourth july , ... joe biden promise delay vaccine turn america prison state locking home let far left rioters roam free biden lockdown mean school graduations weddings thanksgiving christmas fourth july [joe, biden, promise, delay, vaccine, turn, america, prison, state, locking, home, let, far, left, rioters, roam, free, biden, lockdown, mean, school, graduations, weddings, thanksgiving, christmas, fourth, july]
3 11/2/2020 Joe Biden is a globalist who spent 47 years outsourcing your jobs, opening your borders, and sacrificing American blood and treasure in endless foreign wars. He shuttered your steel mills, annihilated your coal jobs, and supported every disastrous trade deal for half a century... [joe, biden, is, a, globalist, who, spent, 47, years, outsourcing, your, jobs, ,, opening, your, borders, ,, and, sacrificing, american, blood, and, treasure, in, endless, foreign, wars, ., he, shuttered, your, steel, mills, ,, annihilated, your, coal, jobs, ,, and, supported, every, disastrous, trade, deal, for, half, a, century, ...] [joe, biden, be, a, globalist, who, spend, 47, years, outsource, your, job, ,, open, your, border, ,, and, sacrifice, american, blood, and, treasure, in, endless, foreign, war, ., he, shutter, your, steel, mill, ,, annihilate, your, coal, job, ,, and, support, every, disastrous, trade, deal, for, half, a, century, ...] [joe, biden, globalist, spend, 47, years, outsource, job, ,, open, border, ,, sacrifice, american, blood, treasure, endless, foreign, war, ., shutter, steel, mill, ,, annihilate, coal, job, ,, support, disastrous, trade, deal, half, century, ...] joe biden globalist spend 47 years outsource job , open border , sacrifice american blood treasure endless foreign war . shutter steel mill , annihilate coal job , support disastrous trade deal half century ... joe biden globalist spend years outsource job open border sacrifice american blood treasure endless foreign war shutter steel mill annihilate coal job support disastrous trade deal half century [joe, biden, globalist, spend, years, outsource, job, open, border, sacrifice, american, blood, treasure, endless, foreign, war, shutter, steel, mill, annihilate, coal, job, support, disastrous, trade, deal, half, century]
4 11/2/2020 I gave Maine everything that Obama/Biden took away from it. 5000 square miles, Lobster, Fishing, ended tariffs from China and E.U. and much more. Vote Trump Maine! [i, gave, maine, everything, that, obama/biden, took, away, from, it, ., 5000, square, miles, ,, lobster, ,, fishing, ,, ended, tariffs, from, china, and, e.u, ., and, much, more, ., vote, trump, maine, !] [i, give, maine, everything, that, obama/biden, take, away, from, it, ., 5000, square, miles, ,, lobster, ,, fish, ,, end, tariff, from, china, and, e.u, ., and, much, more, ., vote, trump, maine, !] [maine, obama/biden, away, ., 5000, square, miles, ,, lobster, ,, fish, ,, end, tariff, china, e.u, ., more, ., vote, trump, maine, !] maine obama/biden away . 5000 square miles , lobster , fish , end tariff china e.u . more . vote trump maine ! maine obama biden away square miles lobster fish end tariff china more vote trump maine [maine, obama, biden, away, square, miles, lobster, fish, end, tariff, china, more, vote, trump, maine]
5 11/2/2020 Landing in Scranton, Pennsylvania! [landing, in, scranton, ,, pennsylvania, !] [land, in, scranton, ,, pennsylvania, !] [land, scranton, ,, pennsylvania, !] land scranton , pennsylvania ! land scranton pennsylvania [land, scranton, pennsylvania]
In [57]:
#Drop all the extra columns created during the data preprocess and cleaning
trumpdfDrop = ['Tweet','tokens','lemma_tokens', 'nostop_tokens', 'nostop_string','cleanedText']
trumpdf.drop(trumpdfDrop, inplace=True, axis=1)
trumpdf.head(3)
Out[57]:
Date cleanTweets
0 11/2/2020 [land, traverse, city, michigan, big, crowd]
1 11/2/2020 [christians, great, country, celebrate, souls, day, let, remember, build, great, nation, legacy, inspire, nation, bless, great]
2 11/2/2020 [joe, biden, promise, delay, vaccine, turn, america, prison, state, locking, home, let, far, left, rioters, roam, free, biden, lockdown, mean, school, graduations, weddings, thanksgiving, christmas, fourth, july]
In [58]:
# Getting the length of tokens in each tokenized tweet 
# convert column of lists to string
cleanTweets_len = []
for i in range(len(trumpdf['cleanTweets'])):
    cleanTweets_len.append(len(trumpdf['cleanTweets'][i]))

trumpdf['cleanTweets_count'] = cleanTweets_len
trumpdf.head(3)
Out[58]:
Date cleanTweets cleanTweets_count
0 11/2/2020 [land, traverse, city, michigan, big, crowd] 6
1 11/2/2020 [christians, great, country, celebrate, souls, day, let, remember, build, great, nation, legacy, inspire, nation, bless, great] 16
2 11/2/2020 [joe, biden, promise, delay, vaccine, turn, america, prison, state, locking, home, let, far, left, rioters, roam, free, biden, lockdown, mean, school, graduations, weddings, thanksgiving, christmas, fourth, july] 27
In [59]:
#Check to see if we have at least one token left after preprocessing and only keep those rows
trumpdf= trumpdf[trumpdf["cleanTweets_count"]>0]

#Get the data dimention
trumpdf.shape
Out[59]:
(1411, 3)
In [60]:
#creating the list for tokens in Trump's tweets for using in wordcloud visualization
trump_tokens = trumpdf['cleanTweets'].tolist()
trump_tokens = [item for sublist in trump_tokens for item in sublist]
In [61]:
#Create picture for trump wordcloud
#Here we use donald trump picture as a mask for wordcloud of hos most frequent words
from PIL import Image
trump_image= np.array(Image.open("trump wordcloud.png"))
In [62]:
#Call the wordcloud function on trump tokens
wordcloud_trump = WordCloud(max_font_size=100, max_words=150,scale=10,relative_scaling=.6, background_color="white", contour_color="black",
                            contour_width= 3, mask= trump_image,colormap ="ocean", collocations=False).generate(', '.join(trump_tokens))

# Display the generated image:
plt.figure(figsize=(12,10))
plt.imshow(wordcloud_trump, interpolation='bilinear')
plt.axis("off")
plt.show()

predicting Trump tweet's polarity using the Logistic regression model

In [63]:
#Convert the cleanTweet tokens to clean text for being used in vectorization
trumpdf['cleanTweets'] = [','.join(map(str, lists)) for lists in trumpdf['cleanTweets']]
In [64]:
##TF-IDF Vectorization on trump dataset
tfidfvec1 = TfidfVectorizer(max_features = 2000, ngram_range=(1,2)).fit(trumpdf.cleanTweets)

# create matrix from the vectorizer
V1 = tfidfvec1.transform(trumpdf.cleanTweets)

# create the transformed df
transformed_df1 = pd.DataFrame(V1.toarray(), columns=tfidfvec1.get_feature_names())
d1 = trumpdf.join(transformed_df1)

#Drop the columns that are not used in the model
d1 = d1.drop(["Date","cleanTweets", "cleanTweets_count" ],axis = 1)
d1.head()
Out[64]:
abc able abolish abolish police absentee absentee ballot absentee vote absolute absolutely abuse accept accord accurate act act fund action actually add addition additional address administration admit admit right ads advance advertise afghanistan african african american again against against fracking agenda agitators agitators anarchists agitators looters ago agree ahead ahead schedule aid air air force airport airport grant airports airtime alabama alaska all all time allegiance allow ally amaze amazon amendment amendment vote america america great american american dream american energy american flag american history american people american workers americans anarchists anarchists agitators anarchists looters andrew angry announce anonymous answer anthem anticipate antifa anybody anymore apart apologize approval approval rat approve arab area areas arizona arkansas arm arrest arrive arsonists art asap ask atlantic attack august author authorization auto avenue aviation award away bad bad rat bad things badly bail bailout bailout money ballot ballot count ballot fraud ballot harvest ballot send baltimore ban ban china ban people barely base basement battle bay beat beautiful beauty beauty parlor bedminster bedminster new begin behind believe ben ben sasse benefit bernie bernie sanders best best world betray better better healthcare beyond biden biden administration biden against biden america biden corrupt biden corruption biden democrats biden destroy biden fail biden massive biden never biden plan biden radical biden raise biden say biden support biden very biden want biden win big big buck big congressional big crowd big day big deal big football big news big pharma big rally big tax big tech big win bigger bigger better biggest biggest history biggest political biggest tax bill billion billion dollars billions billions dollars black black live black people blame bless blood bloomberg blow blue blue state bob bolton book booker boom boost border border security border wall bore box brave break bretbaier bridge brief brilliant bring bring national brown buck build burn bus bus service business businesses buy california called campaign campaign catch canada cancel cancel culture candidate capacity car care care act career carolina carolina soon carry case case case case more case test castro catastrophic catch catch cold celebrate center century certain certainly chad champion chance change chaos charge charles charlie cheat check chicago chief chief staff child children china china plague china virus china win chinavirus chinese choice choose chris chris wallace chuckgrassley church cities cities state citizens city city state claim class classify classify information clause clean clear clinton close closely clue cnbc cnn cnn msdnc cnn poll coast cold cold case colorado columbus combat combine come come fast come very come way comeback comey commerce commercial commission commit commit usdot committee communities community company compare complete complete total completely con con job concast concern condition conference conference today confirm congestion congrats congratulations congress congressional congressman congresswoman connect conservative conservatives consider constantly continue continue operations control convention conversation cop coronavirus correct corridor corrupt corrupt election corrupt fake corrupt joe corrupt media corrupt politician corruption cost costly count counties countries country country flag country history country record country world county course court court justice court justices court let courthouse cover covid covid covid crash crazy crazy bernie crazy nancy create credibility credit crime crime bill crime century crime democrat crime history crime number criminal criminals critical criticize crook crook hillary cross crowd crowd soon cspan culture cuomo cure cut cut tax daca dallas dallas area damage dangerous david day day america days days ago dead deal death death rate deaths deaths way debate decades decision decisions declaration defeat defend defend second defense defund defund abolish defund police delay deliver dem demand demean democrat democrat cities democrat governor democrat mayor democrat national democrat run democrats democrats hold democrats try democrats want dems deny department departments depression deserve designate designate million desperate desperately desperately need despite destroy destroy america destroy country destruction devastate develop development dhsgov die difference different difficult direction director dirty dirty cop disagree disaster disastrous discuss disgrace disgraceful disgruntle disgruntle employee dishonest disinformation disinformation campaign disrespect dnc doctor document dog dollars dollars tax dominate donald donald trump drain drain swamp draw dream drop drug drug company drug price dumb early early ban earth easier easily east eastern easy eat economic economic growth economic recovery economy economy job education elect elected election election history elections eliminate elizabeth elizabeth warren embarrassment emergency employee end endless endless war endorse endorse sleepy endorsement enemy energy energy price enforcement enjoy enjoy foxnews ensure enter enthusiasm entire entry equal especially establish establishment etc europe everybody everybody know evidence examples excite execute executive executive order exercise exist expand expect expensive explain extend face fact facts fail fail badly fail nytimes fail presidential failure fair fake fake news fake poll fake suppression fall false families family fan fantastic fantastic job far far better far leave far less far more far most farmers fast faster fauci fault favor favor nations favorite favorite president fbi fda federal federal fund federal government federal infrastructure federal property feel felt fema few fewer fight fight small fighter final finally find finest finish fire fisa fish fix flag flee flint flint water flood florida floyd flu flynn focus follow food fool football force ford foreign foreign countries foreign war forever forget form former former governor former president fort forward fox foxandfriends foxandfriends enjoy foxbusiness foxnews foxnews enjoy foxnews oann foxnews poll foxnews thank fracking france fraud fraudulent fredo free freedom friend friendly friendly protesters friends fully fund fund help fund support fund usdot funny future gain gallup game garbage garcia gateway gdp general general flynn george george floyd georgia germany giant gift glad god golf good good news government government help governor governor cuomo governors graduate grant great great again great american great country great guy great honor great job great leader great men great military great national great new great news great people great red great republican great senator great state great things great work greater greatest greatest political greatly greatness grind grossly grossly incompetent group growth guarantee guard guard great gun guy hack half hall hampshire hand handle handle china handle coronavirus handle swine handlers happen happy happy support harass hard harder hardly harm harris harvest hate hater haters hatred head health healthcare hear hearts heavily hell help help people heritage hero hiden high high crime higher highest highly highly respect highway hike hillary hillary clinton hispanic historic history history country hit hit job hoax hold home homeland homeland security honest honor hope hopefully horrible hospital hospitals house house news huge hunt hurricane hurt husband hydroxychloroquine idea iden ill illegal illegally illinois immediately immigration impeachment important important election importantly impossible improve improvements include include many income income house incompetence incompetent increase incredible incredible job incredible people india indiana individual indoctrinate industry infect infect china influence info inform information infrastructure infrastructure fund ingrahamangle innocent instead interchange interest international interview interview foxandfriends interview mariabartiromo interview seanhannity investigation investigations involve iowa iran islamic israel issue jail jam jam comey japan jeff jeff sessions jersey jim job job growth job number job people joe joe biden joe hiden joe scarborough john john bolton join joke jong journal judge july justice justices ken kenosha kenosha wisconsin kentucky kill kim kim jong kind know knowledge lack lady lake lamestream lamestream media land land wisconsin large large scale largest late late term later launch law law enforcement law order lawless lawlessness lead lead massive lead most leader leaders leadership leak learn least leave leave anarchists leave democrat leave democrats lecture leepy leepy joe left legendary less let let happen lethal letter letter resignation level lewis liberal liberal democrat lie life like like big like crazy like never likewise line list listen little live live matter local locations lockdowns long long time longer look look bad look forward look good look great look like look very loot looters lose loser losers loss lot lot money lou loudobbs louisiana love love great love military lover low low energy low income low tax lower lower cost lower drug lowest lowlife lowlifes loyal maga magazine magnificent mail mail ballot mail vote maine major majority man mandate many many countries many millions many people many state many things many years mariabartiromo mark market market big marklevinshow mask mass massive massive disinformation massive number massive tax massively matter maybe mayor mayor governor mayor portland mayorbowser mayors mccabe mccain mean media media big media want medical medical center medicare medicare premiums meet member members memorial memory men men women mention merely mess message miami michael michigan middle middle east mike mike bloomberg mike garcia mike pence miles military military base military vet million million people million usdot millions millions dollars mini mini mike minister minneapolis minnesota minutes miss mississippi mistake mitt mitt romney monday money monitor montana month months monuments more more biden more case more enthusiasm more like more money more people more test morning morning joe mortality mortality rate most most corrupt most important msdnc much much needed mueller murder nancy nancy pelosi nasdaq nasdaq hit nation national national anthem national convention national guard nations nations clause navy nbcnews nearly nebraska necessary need needed negative neighborhood nevada never never forget never hear never let never like never trumper new new book new hampshire new jersey new record new york news news cnn news conference news lamestream news media news refuse news report newsmax nfl nice night nomination north north carolina notice november number number come number look number people numerous nurse nurse home nyc nycmayor nygovcuomo nygovcuomo nycmayor nypost nytimes oann oann cspan oann newsmax obama obama administration obama biden obama sleepy obamabiden obamacare obamagate obviously office officer officials ohio oil oklahoma old online open open border open cold open school operations opinion opponent oppose opposite order order protect oregon organization organizations outside overall overtime owner pack pack court paint pandemic paper park parlor parlor owner particular partner party party approval party thank pass past pathetic patients patterson pay payments payroll payroll tax peace peaceful pelosi pelosi say pence pennsylvania pennsylvania big people people alabama people arrest people come people die people flee people great people many people need people new people request people sick people want people wisconsin people work person pharma philadelphia phone phony picture pig pittsburgh place place fake plague plain plan plant platform play play game play golf play right players pledge pledge allegiance plenty plunge plus pocahontas point police police departments police officer policies policy political political crime political reason political scandal politically politician politicians politics poll poll bad poll fake poll number poll place pollster poor poorly poorly run popular population port portland position positive possible post post office potential powell power powerful prayers pre premiums prepay prescription prescription drug president president history president obama president trump president unite presidential presidential candidate press press conference price price come primaries primary prime prime minister print prior prison probably problem problems process production program progress project promise promote proof propaganda properly property propose propose award prosecute prosperity protect protection protest protesters protestors proud proud announce prove provide psycho psycho joe public puerto puerto rico pull puppet pure purpose push quarter question quick quickly race radical radical leave rail raise raise tax rally rally today rally tonight rampant rapidly rasmussen rasmussen poll rat rat republican rate reach read ready ready land ready send real real poll realize reason reat rebuild receive receive federal receive usdot recent recently reconstruct record record set recover recovery red red wave reduce reduction reed refer refugees refuse region regulate regulation release religion remain remark remember remember vote remove rename reopen repeal repeal section replace report reporter represent representatives republican republican national republican party republicans request residents resignation respect response responsible rest result return reveal review rick rick snyder rico ridiculous rig rig election right right hand rino riot rioters rioters looters rip rise risk robert roger rogue role roll romney ronny ronnyjacksontx room round round turn ruin rule run run against run cities run democrat run president russia russia hoax russia russia russian ryan sacrifice sad sadden sadly safe safely safety safety security sally sally yates sample sanctuary sanders sasse saturday saturday night save save live saw say say know say set say want scale scam scandal scandal history scarborough schedule school school choice school open schumer scranton scully sean seanhannity seanhannity foxnews seanhannity tonight seanparnellusa season seattle second second amendment second debate secretary section secure security self senate senatemajldr senator senators send send ballot send million send usdot seniors sentence september serve service service people sessions set set receive shame shape shoot shortly shut shutdown sick sign sign order significantly silent silent majority simple single site situation sleepy sleepy joe slow slow joe small small business small businesses smaller smart smarter snyder social soldier solve somebody soon sooo sorry sound source speak special special interest speech speed spend spend years spread spy spy campaign square square miles staff stand stand national star start state state america state ask state great state local state michigan state new state north state open state supreme state texas state wisconsin statement statements statue statues stay stay safe steal steal election step steve steve scully stevehiltonx stevehiltonx foxnews stiff stimulus stock stock market stone stop store stories story street street journal streets strength strong strong crime stronger strongly study stun stupid stupidity subject substantially suburban suburban women suburbs succeed success successful sudan suffer suggest summer sunday super support support bus supporter supporters suppose suppression suppression poll supreme supreme court sure suspend swamp swine swine flu system takeover talk tampa tap tap tap tape target tax tax border tax cut tax hike tax increase tax protect tax regulation taylor team tech television television rat tell tennessee tens term term abortion terminal terminate terrible terror terrorist terrorists test test country texas thank thank president thank very therapeutics thing thing happen things things happen think think possible thousands thousands people throw thugs thursday tiffany time time fake time high tire today today great together tom tom tiffany tomorrow tonight tonight enjoy tonight foxnews tony total total disaster total endorsement total fraud total mess totally totally control tough tougher town town hall trade trade deal traffic tragic train transit transit project transit service transit system transition transition greatness transportation travel treason treat treat like tremendous tremendous job tremendous progress trend trillion trillion dollars trip troop trouble true truly trump trump campaign trumper trust truth try try steal tuckercarlson tuesday tulsa tulsa oklahoma turn twice twitter understand unemployment unfair unfortunately union unite unite state universal universal mail unlike unlimited unsolicited unsolicited ballot update upgrade upset upside usa usa history usa want usdot usdot commit usdot fund usdot help usdot reconstruct use use mail use term usual utah vaccine vaccines vaccines therapeutics vandalize varneyco ventilators very very bad very early very few very good very hard very important very late very poor very proud very smart very strong very tough very unfair vet vet low veteran vice vice president victory view violence violent virginia virus visit voice vote vote corrupt vote count vote radical vote trump vote vote voter voters wacko wacko john wacky wait walk wall wall street wallace wallace foxnews walter walter reed want want defund want law want open want pack want safety war warren warrior washington washingtonpost washtimes watch watch fake watch happen water wave wave come way ways weak weakness week weekend weeks west wheeler white white house wife win win against win america win biden win big win china win election winner wisconsin wish witch witch hunt witness woman women wonder wonderful woodward word work work hard work overtime work together work very workers world world war worse worst wow write wrong wsj xaa xaf xaf xaf xaf xdb xda xda xaf xdb xdb xdb yates year year best years years ago years fake years great years prison yesterday york york city york finest york time young zero zone
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In [65]:
#Call the logistic regression model on trump data to predict the sentimnets of his tweets and store them on the predicted
#sentiment column
trumpdf['predicted_sentiment'] = log_reg.predict(d1)
#Display the first 6 rows
trumpdf.head(6)
Out[65]:
Date cleanTweets cleanTweets_count predicted_sentiment
0 11/2/2020 land,traverse,city,michigan,big,crowd 6 0
1 11/2/2020 christians,great,country,celebrate,souls,day,let,remember,build,great,nation,legacy,inspire,nation,bless,great 16 0
2 11/2/2020 joe,biden,promise,delay,vaccine,turn,america,prison,state,locking,home,let,far,left,rioters,roam,free,biden,lockdown,mean,school,graduations,weddings,thanksgiving,christmas,fourth,july 27 1
3 11/2/2020 joe,biden,globalist,spend,years,outsource,job,open,border,sacrifice,american,blood,treasure,endless,foreign,war,shutter,steel,mill,annihilate,coal,job,support,disastrous,trade,deal,half,century 28 1
4 11/2/2020 maine,obama,biden,away,square,miles,lobster,fish,end,tariff,china,more,vote,trump,maine 15 1
5 11/2/2020 land,scranton,pennsylvania 3 1
In [66]:
#Decoding the binary labels to Positive and Negative sentiment label for the visualization purpose 
decode_map = {0: "Negative", 1: "Positive"}
def sentiment_decode(label):
    return decode_map[int(label)]
trumpdf.predicted_sentiment = trumpdf.predicted_sentiment .apply(lambda x: sentiment_decode(x))
trumpdf.head(3)
Out[66]:
Date cleanTweets cleanTweets_count predicted_sentiment
0 11/2/2020 land,traverse,city,michigan,big,crowd 6 Negative
1 11/2/2020 christians,great,country,celebrate,souls,day,let,remember,build,great,nation,legacy,inspire,nation,bless,great 16 Negative
2 11/2/2020 joe,biden,promise,delay,vaccine,turn,america,prison,state,locking,home,let,far,left,rioters,roam,free,biden,lockdown,mean,school,graduations,weddings,thanksgiving,christmas,fourth,july 27 Positive
In [67]:
#Print the percentage of positive and negative tweets of Donald Trump
tPositive_sents= round(len(trumpdf[trumpdf['predicted_sentiment']== "Positive"])*100/len(trumpdf['predicted_sentiment']), 2)
tNegative_sents=100-tPositive_sents

print("Trump Positive Sentiment percentage is:")
print(tPositive_sents , "%")    
print("Trump Negative Sentiment percentage is:")
print(tNegative_sents , "%")
Trump Positive Sentiment percentage is:
53.37 %
Trump Negative Sentiment percentage is:
46.63 %

Joe Biden Tweets Data

In [68]:
#Read the Biden dataset as dataframe 
bidendf = pd.read_csv('C:/Users/ellie/OneDrive/Documents/GMU/AIT590-Natural Language Processing/Project/Biden.csv', encoding = "ISO-8859-1")
bidendf.head(6)
Out[68]:
Date Tweet
0 11/2/2020 Donald Trump is the most corrupt president in modern history. Donald Trump is the most racist president in modern history. Donald Trump is the worst jobs president in modern history. Why would we give him another four years?
1 11/2/2020 Here's the truth: Donald Trump inherited a growing economy from President Obama and me. And just like everything else he's inherited in life, he squandered it.
2 11/2/2020 When America votes, America will be heard. And when America is heard, I believe the message is going to be loud and clear: It's time for Donald Trump to leave the White House.
3 11/2/2020 A Biden-Harris administration will: - Implement nationwide mask mandates - Ensure access to regular, reliable, and free testing - Accelerate the development and distribution of safe and effective treatments and vaccines We won't waste any time getting this virus under control.
4 11/2/2020 Together, we're going to rebuild our economy. And when we do, we'll not only build it back - we'll build it back better.
5 11/2/2020 In public, President Trump compared COVID-19 to the flu and suggested people inject bleach to treat it. In private, he told Bob Woodward it was deadlier than the flu and that he wanted to downplay it. It's unthinkable.
In [69]:
#Get the dimention of biden tweets dataset
bidendf.shape
Out[69]:
(578, 2)
In [70]:
#check the number of duplicate rows
bidenrow = bidendf[bidendf.duplicated()]
print("Duplicate Rows:")
print(bidenrow)
# ==> There is no duplicate
Duplicate Rows:
Empty DataFrame
Columns: [Date, Tweet]
Index: []
In [71]:
#check the number of missing values 
bidendf.isnull().sum()
# ==> There is no missing values
Out[71]:
Date     0
Tweet    0
dtype: int64
In [72]:
#Tokenize the tweets using the tokenize function defined earlier in the code in section 1
bidendf['tokens'] = bidendf['Tweet'].apply(lambda x: tokenize_text(x.lower()))
In [73]:
#Lemmatize tweets ussing the pre-defined function for lemmatizing the tokens in section 1
bidendf['lemma_tokens'] =bidendf['tokens'].apply(lambda x: lemma(x))
In [74]:
#Remove stopwords using the pre-defined function for removing stopwords in section 1
bidendf['nostop_tokens'] = bidendf['lemma_tokens'].apply(lambda x: remove_stopwords(x))
In [75]:
#Put back the nostop_tokens to sentence format for enabling to use punctuation removal later
bidendf['nostop_string'] = [' '.join(map(str, lists)) for lists in bidendf['nostop_tokens']]
In [76]:
#Remove all punctuations, mentions, url links and hashtags using the function defined earlier in section 1
bidendf['cleanedText'] = bidendf['nostop_string'].apply(lambda x: remove_punctuation(x))
In [77]:
#Remove all short words that has no meaning and does contribute 
bidendf['cleanedText'] = bidendf['cleanedText'].apply(lambda x: ' '.join([w for w in x.split() if len(w) > 2]))
In [78]:
#Tokenize the tweets after all above cleaning process 
bidendf['cleanTweets'] = bidendf['cleanedText'].apply(lambda x: tokenize_text(x.lower()))
bidendf.head(6)
Out[78]:
Date Tweet tokens lemma_tokens nostop_tokens nostop_string cleanedText cleanTweets
0 11/2/2020 Donald Trump is the most corrupt president in modern history. Donald Trump is the most racist president in modern history. Donald Trump is the worst jobs president in modern history. Why would we give him another four years? [donald, trump, is, the, most, corrupt, president, in, modern, history, ., donald, trump, is, the, most, racist, president, in, modern, history, ., donald, trump, is, the, worst, jobs, president, in, modern, history, ., why, would, we, give, him, another, four, years, ?] [donald, trump, be, the, most, corrupt, president, in, modern, history, ., donald, trump, be, the, most, racist, president, in, modern, history, ., donald, trump, be, the, worst, job, president, in, modern, history, ., why, would, we, give, him, another, four, years, ?] [donald, trump, most, corrupt, president, modern, history, ., donald, trump, most, racist, president, modern, history, ., donald, trump, worst, job, president, modern, history, ., years, ?] donald trump most corrupt president modern history . donald trump most racist president modern history . donald trump worst job president modern history . years ? donald trump most corrupt president modern history donald trump most racist president modern history donald trump worst job president modern history years [donald, trump, most, corrupt, president, modern, history, donald, trump, most, racist, president, modern, history, donald, trump, worst, job, president, modern, history, years]
1 11/2/2020 Here's the truth: Donald Trump inherited a growing economy from President Obama and me. And just like everything else he's inherited in life, he squandered it. [here, 's, the, truth, :, donald, trump, inherited, a, growing, economy, from, president, obama, and, me, ., and, just, like, everything, else, he, 's, inherited, in, life, ,, he, squandered, it, .] [here, 's, the, truth, :, donald, trump, inherit, a, grow, economy, from, president, obama, and, me, ., and, just, like, everything, else, he, 's, inherit, in, life, ,, he, squander, it, .] [truth, :, donald, trump, inherit, grow, economy, president, obama, ., like, inherit, life, ,, squander, .] truth : donald trump inherit grow economy president obama . like inherit life , squander . truth donald trump inherit grow economy president obama like inherit life squander [truth, donald, trump, inherit, grow, economy, president, obama, like, inherit, life, squander]
2 11/2/2020 When America votes, America will be heard. And when America is heard, I believe the message is going to be loud and clear: It's time for Donald Trump to leave the White House. [when, america, votes, ,, america, will, be, heard, ., and, when, america, is, heard, ,, i, believe, the, message, is, going, to, be, loud, and, clear, :, it, 's, time, for, donald, trump, to, leave, the, white, house, .] [when, america, vote, ,, america, will, be, hear, ., and, when, america, be, hear, ,, i, believe, the, message, be, go, to, be, loud, and, clear, :, it, 's, time, for, donald, trump, to, leave, the, white, house, .] [america, vote, ,, america, hear, ., america, hear, ,, believe, message, loud, clear, :, time, donald, trump, leave, white, house, .] america vote , america hear . america hear , believe message loud clear : time donald trump leave white house . america vote america hear america hear believe message loud clear time donald trump leave white house [america, vote, america, hear, america, hear, believe, message, loud, clear, time, donald, trump, leave, white, house]
3 11/2/2020 A Biden-Harris administration will: - Implement nationwide mask mandates - Ensure access to regular, reliable, and free testing - Accelerate the development and distribution of safe and effective treatments and vaccines We won't waste any time getting this virus under control. [a, biden-harris, administration, will, :, -, implement, nationwide, mask, mandates, -, ensure, access, to, regular, ,, reliable, ,, and, free, testing, -, accelerate, the, development, and, distribution, of, safe, and, effective, treatments, and, vaccines, we, wo, n't, waste, any, time, getting, this, virus, under, control, .] [a, biden-harris, administration, will, :, -, implement, nationwide, mask, mandate, -, ensure, access, to, regular, ,, reliable, ,, and, free, test, -, accelerate, the, development, and, distribution, of, safe, and, effective, treatments, and, vaccines, we, wo, n't, waste, any, time, get, this, virus, under, control, .] [biden-harris, administration, :, -, implement, nationwide, mask, mandate, -, ensure, access, regular, ,, reliable, ,, free, test, -, accelerate, development, distribution, safe, effective, treatments, vaccines, wo, waste, time, virus, control, .] biden-harris administration : - implement nationwide mask mandate - ensure access regular , reliable , free test - accelerate development distribution safe effective treatments vaccines wo waste time virus control . biden harris administration implement nationwide mask mandate ensure access regular reliable free test accelerate development distribution safe effective treatments vaccines waste time virus control [biden, harris, administration, implement, nationwide, mask, mandate, ensure, access, regular, reliable, free, test, accelerate, development, distribution, safe, effective, treatments, vaccines, waste, time, virus, control]
4 11/2/2020 Together, we're going to rebuild our economy. And when we do, we'll not only build it back - we'll build it back better. [together, ,, we, 're, going, to, rebuild, our, economy, ., and, when, we, do, ,, we, 'll, not, only, build, it, back, -, we, 'll, build, it, back, better, .] [together, ,, we, 're, go, to, rebuild, our, economy, ., and, when, we, do, ,, we, 'll, not, only, build, it, back, -, we, 'll, build, it, back, better, .] [together, ,, rebuild, economy, ., ,, build, -, build, better, .] together , rebuild economy . , build - build better . together rebuild economy build build better [together, rebuild, economy, build, build, better]
5 11/2/2020 In public, President Trump compared COVID-19 to the flu and suggested people inject bleach to treat it. In private, he told Bob Woodward it was deadlier than the flu and that he wanted to downplay it. It's unthinkable. [in, public, ,, president, trump, compared, covid-19, to, the, flu, and, suggested, people, inject, bleach, to, treat, it, ., in, private, ,, he, told, bob, woodward, it, was, deadlier, than, the, flu, and, that, he, wanted, to, downplay, it, ., it, 's, unthinkable, .] [in, public, ,, president, trump, compare, covid-19, to, the, flu, and, suggest, people, inject, bleach, to, treat, it, ., in, private, ,, he, tell, bob, woodward, it, be, deadlier, than, the, flu, and, that, he, want, to, downplay, it, ., it, 's, unthinkable, .] [public, ,, president, trump, compare, covid-19, flu, suggest, people, inject, bleach, treat, ., private, ,, tell, bob, woodward, deadlier, flu, want, downplay, ., unthinkable, .] public , president trump compare covid-19 flu suggest people inject bleach treat . private , tell bob woodward deadlier flu want downplay . unthinkable . public president trump compare covid flu suggest people inject bleach treat private tell bob woodward deadlier flu want downplay unthinkable [public, president, trump, compare, covid, flu, suggest, people, inject, bleach, treat, private, tell, bob, woodward, deadlier, flu, want, downplay, unthinkable]
In [79]:
#Drop all the extra columns created during the data preprocess and cleaning
bidendfDrop = ['Tweet','tokens','lemma_tokens', 'nostop_tokens', 'nostop_string','cleanedText']
bidendf.drop(bidendfDrop, inplace=True, axis=1)
bidendf.head(3)
Out[79]:
Date cleanTweets
0 11/2/2020 [donald, trump, most, corrupt, president, modern, history, donald, trump, most, racist, president, modern, history, donald, trump, worst, job, president, modern, history, years]
1 11/2/2020 [truth, donald, trump, inherit, grow, economy, president, obama, like, inherit, life, squander]
2 11/2/2020 [america, vote, america, hear, america, hear, believe, message, loud, clear, time, donald, trump, leave, white, house]
In [80]:
# Getting the length of tokens in each tokenized tweet 
# convert column of lists to string
cleanTweets_len = []
for i in range(len(bidendf['cleanTweets'])):
    cleanTweets_len.append(len(bidendf['cleanTweets'][i]))

bidendf['cleanTweets_count'] = cleanTweets_len
bidendf.head(3)
Out[80]:
Date cleanTweets cleanTweets_count
0 11/2/2020 [donald, trump, most, corrupt, president, modern, history, donald, trump, most, racist, president, modern, history, donald, trump, worst, job, president, modern, history, years] 22
1 11/2/2020 [truth, donald, trump, inherit, grow, economy, president, obama, like, inherit, life, squander] 12
2 11/2/2020 [america, vote, america, hear, america, hear, believe, message, loud, clear, time, donald, trump, leave, white, house] 16
In [81]:
#creating the list for tokens in biden's tweets for using in wordcloud visualization
biden_tokens = bidendf['cleanTweets'].tolist()
biden_tokens = [item for sublist in biden_tokens for item in sublist]
In [82]:
#Create picture for biden wordcloud
#Here we use Joe Biden picture as a mask for wordcloud of hos most frequent words
from PIL import Image
biden_image= np.array(Image.open("biden wordcloud.png"))
biden_image =np.where(biden_image>3, 255, biden_image)
In [83]:
##Call the wordcloud function on biden tokens
wordcloud_biden= WordCloud(max_font_size=100, max_words=150,scale=10,relative_scaling=.6, background_color="white", contour_color="black",
                            contour_width= 3, mask= biden_image,colormap ="ocean", collocations=False).generate(', '.join(biden_tokens))

# Display the generated image:
plt.figure(figsize=(15,10))
plt.imshow(wordcloud_biden, interpolation='bilinear')
plt.axis("off")
plt.show()

predicting Biden tweet's polarity using the Logistic regression model

In [84]:
#Convert the cleanTweet tokens to clean text for being used in vectorization
bidendf['cleanTweets'] = [','.join(map(str, lists)) for lists in bidendf['cleanTweets']]
bidendf.head(6)
Out[84]:
Date cleanTweets cleanTweets_count
0 11/2/2020 donald,trump,most,corrupt,president,modern,history,donald,trump,most,racist,president,modern,history,donald,trump,worst,job,president,modern,history,years 22
1 11/2/2020 truth,donald,trump,inherit,grow,economy,president,obama,like,inherit,life,squander 12
2 11/2/2020 america,vote,america,hear,america,hear,believe,message,loud,clear,time,donald,trump,leave,white,house 16
3 11/2/2020 biden,harris,administration,implement,nationwide,mask,mandate,ensure,access,regular,reliable,free,test,accelerate,development,distribution,safe,effective,treatments,vaccines,waste,time,virus,control 24
4 11/2/2020 together,rebuild,economy,build,build,better 6
5 11/2/2020 public,president,trump,compare,covid,flu,suggest,people,inject,bleach,treat,private,tell,bob,woodward,deadlier,flu,want,downplay,unthinkable 20
In [85]:
#TF-IDF Vectorization on biden dataset
tfidfvec2 = TfidfVectorizer(max_features = 2000, ngram_range=(1,2)).fit(bidendf.cleanTweets)

# create matrix from the vectorizer
V2 = tfidfvec2.transform(bidendf.cleanTweets)
# print(V[0:5])
# create the transformed df
transformed_df2 = pd.DataFrame(V2.toarray(), columns=tfidfvec2.get_feature_names())
d2 = bidendf.join(transformed_df2)
d2 = d2.drop(["Date","cleanTweets", "cleanTweets_count"],axis = 1)
d2.head()
Out[85]:
abandon ability able abuse accelerate accelerate development accept access access care access free access health access regular accessible accomplish accomplish together accountable achieve act act sign action action contain action ensure action need actively actually actually listen address administration administration slow administration work affect affirm afford afford years affordable affordable accessible african african american again again donald against against common ago ago today ago white ahead ahead come ahead together allow ally alongside alter alter character ambition amendment america america come america emerge america found america hear america president america time america word american american able american access american community american deserve american economy american future american history american live american manufacture american people american president american workers americans americans die americans live americans look americans lose americans pay americans pre americans unemployment americans vote americans work and angels angels prevail anniversary anniversary amendment announce answer anti apart appeal appeal best apply approach april arm aside ask ask stay assault assault weapons assistance attack august avenue avenue come away away millions bad ballot ban ban assault bank basic basic duty battle battle soul battle win bear bear responsibility beat beat virus beau beg beg president begin beginnings behind behind covid believe believe best believe define believe say believe science benefit best best america best days best wish best worst betsy betsy devos better better angels better create better future biden biden america biden harris big big bank big corporations bigger biggest bigotry bill bill ensure billion black black americans black brown blake blame blame never bless blue blue state bob bob woodward brave break bridge bring bring together brown build build better build economy build new build simple build together build way business businesses businesses close but buy california callously callously use campaign campaign rally campaign say capacity capacity magazines care care need care protections care right care system care workers career carry case case covid cdc celebrate challenge challenge history chance chance ahead change change tone character character nation charge charge defeat charlottesville chart chart path cheerleader cheerleader need child choice chokeholds choose choose hope choose run choose unite church citizens civil claim class clear clear donald clear pay clearer clench clench fists climate climate change climate crisis close closer closer more color combat combat climate come come president come scranton come together commit commitment common common threats communities community community never compassion concern condition condolences confirm confirm case congress connected consequences constitution constitution promise contain contain spread contain virus continue continue fight continue prove continue work control control virus cop coronavirus coronavirus test corporations corporations finally corrupt corrupt president corruption cost cost live countless countless americans countless small country country know country middle country people country work court cover covid covid case covid crisis covid death covid donald covid million covid pose covid response covid surge covid test covid vaccine create create equal crime crises crises emerge crises need crisis crisis health crisis lifetime crisis way critical crowd cry cry leadership crystal crystal clear cuba cut cut short dad dark darkness daughter day day accept day donald day let day president day white days days let days lie deadliest deadly deal death death family deaths debate decide decision decisions decline deep deeply defeat defeat virus defend defense define define america defund deliver demconvention democracy democrat democrat american democratic democrats democrats republicans deny derechos derechos humanos deserve deserve access deserve fair deserve know deserve president deserve voice destroy devastation development development treatments devos die die covid die virus different differently dignity dignity respect disappear disaster distance diversity divide divide more division division drive dog dog whistle domestic domestic violence donald donald trump downplay downplay threat draw drbiden dream dreamers drive drive apart drug duty duty american duty nation earlier early early vote easy economic economic crisis economy economy build economy fair economy work edge education educator effective efforts elect elect president election election battle election day elections eligible eligible american eliminate eliminate obamacare emerge emerge stronger empower enact end end gun end meet end president end year enemy enormous enough ensure ensure access ensure equal ensure never enter entire entire nation epidemic equal equal chance equal pay equality equality justice equality liberty equality opportunity equally era essential essential workers exactly example excuse existential existential threat existing existing condition expand expand access experience experts experts leaders experts like expire extend extraordinary face face greatest face worst fact fade fade away fail fail action fail leadership fail most fail protect failure failure presidential failures fair fair elections fair return fair share fair shoot fairytale faith fall families families pay family family community family family family neighbor far farmers fauci fauci ask favorite fear fear peace federal federal income feel fellow fellow americans festival few few days fiction fight fight day fight president fight single fight tooth fighter file fill final finally finally live finally pay find finish finish strong fire firmly firmly believe fists fit fix flag flame flame hatred flat flat out florida floyd flu focus folks folks country folks long follow follow science food force forever forever alter forget forget job forward found found promise free free test freedom friends friends neighbor fully fully live fund fundamental future future build future time future want gain general generational generations generosity generosity greed george george floyd giant gig ginsburg girls global global pandemic god god create golf good good enough govern government government work governor grapple grateful great great honor greatest greatest challenge greatest failure greatest job greece greece turkey greed grief grow grow scranton guarantee gun gun violence gut guy half half chance hand happen happen again happy harbor hard hard truth hard work hardworking hardworking americans harris harris administration harvest hashtag hate hate safe hatred hatred division head heal health health care health crisis health experts health insurance health safety hear heart hearts help heroes hide hide truth high high capacity high quality higher highest highest ideals hindu hindu festival history history donald history fairytale hit hit president hold hold accountable holy home honor honor carry honor elect hop hope hope fear hopeful horrific hour house house forever house pass human human right human toll humanity humanos hurricane hurricane maria hurt idea idea men ideals identity ignore imagine imagine future immediately immense immigrants impact implement implement national implement nationwide important improve incite incite violence income income tax incompetence increase incredible incredible vice independence independence day independents infections inflection inflection point inherit inherit life initiative inspire instead instead lead insurance interest international invest island issue jacob jacob blake jill jill love jill pray jill send jill wish job job need job president joe joe biden john john lewis join joy july june justice justice america justice ginsburg justice help justice need kamala kamalaharris kamalaharris believe kid kind know know forget know important know kamalaharris know thing know time labor lady lago lago crowd later latinas latino launch launch campaign law law order lead lead charge lead example lead nation lead world leaders leaders party leadership leadership leadership leadership nation leadership president leadership right leadership unite learn leave leave behind leave office less let let clear let donald let end let happen let tell let win let work lewis lewis vote lgbtq lgbtq people lgbtq right liberty liberty justice lie lie ahead life life better lifetime light light darkness like like fauci like inherit like president line listen listen experts listen scientists little little girls live live cut live day live end live highest live lose live today local long long overdue long past longer look los lose lose home lose live lose love lose nearly lot lot more love love ones low lower magazines mail malarkey mandate mandate ensure manufacture many many time mar mar lago march maria mark mark anniversary mark years martin mask mask mandate mask public massacre mate matter matter time mean meet member members memory men men women mental mental health message middle middle class middle global middle pandemic million million americans million case millions millions americans millions morally mind minimum minimum wage minute minute waste mission mistake model modern modern history moment moment action moment crisis moment face moments month months months crisis months pandemic morally morally reprehensible more more americans more divide more federal more million more need more perfect more powerful more tax more violent more years morning most most basic most corrupt mouth movement murder muslim muslim friends nail nation nation battle nation believe nation donald nation history nation let nation president nation stand nation turbulent national national test nationwide nationwide mask nationwide test nearly nearly americans nearly million nearly small need need action need alongside need build need cheerleader need government need job need justice need national need president need real need restore need spend negative neighbor never never forget never fully never let never stop new new american new beginnings new year nicaragua night nomination nomination president not november nra numb number obama obama leave obamacare obamacare rip obamacare years offer office officer officials old one ones online open open arm opportunities opportunity optimistic order ordinary out out ignore outset outset election outside oval oval office overcome overcome crises overdue overlook oversight overtime overtime pay oxygen pace page pain pain lose pandemic pandemic donald pandemic president pandemics pandemics flat park park avenue partner party party chart paso pass pass bill pass john past past time pat path path forward patriot patriotic pay pay fair pay more pay penny pay price peace peace violence pennsylvania pennsylvania fight penny penny more people people accomplish people die people protect people puerto people risk people today people together people tough people treat people turn people understand people value people ways people world perfect perfect union period period family perish perish train permanent permanent closure perpetrate perpetrate deadliest perpetuate perpetuate cycle person person affirm person covid person early person fair person help person lead person look person night person tomorrow person wonder phase phase progress philadelphia philadelphia pennsylvania photo photo donald pick pick kamalaharris pick mistake pick president pick ready pick run pick say pittsburgh pittsburgh tree place place america place communities place hate place never plan plan address plan beat plan contain plan control plan country plan tax plan today plan virus plane plane more planet planet more platforms platforms leave play play golf play role playbook playbook deal playbook president pledge pledge mission plenty plenty good point point choose point direction point way poison poison air poison systems poisonous poisonous ideology pol pol xadticos poland poland solidarity police police force police reform police reverse police violence policies policies cave polish polish americans polish people political political cost political gain political party political patriotic political prisoners political tool political weapon politics politics let politics play pollute pollute consequences poorer poorer more pose pose existential pose fail pose ignore pose refuse position position gain positive positive covid possibilities possibilities america possibilities longer possibilities prosperity possibilities refuse possible possible people possible person possible vaccine possibly possibly imagine postal postal service postpone postpone possibilities potential potentially potentially new poverty poverty grow power power campaign power chadwickboseman power create power ensure power increase power officer power say power shape power supreme power surge power write powerful powerful darkness powerful fear powerful hate powerful let powerful racial powerful reminder ppe ppe clear ppe enough ppe health ppe shortages ppe tee practice practice end prague prague spring praise praise john pray pray health prayers prayers follow prayers people pre pre existing precautions premiums prepare presidency president president actually president americans president believe president care president choose president country president expand president fail president fight president god president job president know president lead president listen president matter president modern president more president most president obama president president president promise president protect president step president tinker president trump president understand president unite president use president vice president vote presidential presidential leadership presidents prevail prevent price prisoners privilege privilege few pro profit program progress promise promise america promise elect promise equality promise most promise nation promise need promise never promise president promise prevent promise protect propel propel nation prosperity protect protect america protect build protect public protect rebuild protect sacred protect state protect strengthen protect trust protect unforgivable protection protection law protection most protections protections away protections benefit protections millions protections unions protective protective equipment protest protest outside protester protester president proud proud champion proud democrat proud free proud kamala proud people proud tonight prouder prove prove fighter prove unfit provide provide leadership provide protection public public health public option public period public president public protect public school public servants public transit public transportation pueblo pueblo cubano puerto puerto rico pulaski pulaski heroism pull pull apart pull country pull more punish punish lockdown punish time purpose purpose felt pursue pursue peace pursuit push quality quit quit america race racial racial justice racism raise raise minimum rally reach ready ready lead ready work real real leadership real police realdonaldtrump reason rebuild rebuild economy recession recognize recommit record recover recovery red red state reflect reform reform police refuse refuse action refuse listen registration regular regular reliable release reliable reliable free relief remain remember remind reminder repeat report reprehensible represent republican republicans republicans independents republicans try require respect response responsibility responsibility fail responsible rest restore restore dignity return return work reverse reward reward work rico right right act right choose right human right privilege right vote rip rip away rip health rise risk root round round golf run run against run mate run proud run senate rush sacred sacred right sacrifice safe safe covid safe effective safe harbor safe joe safely safer safety save save live saw say say battle say outset say tonight say want scar school science science fiction scientists scourge scranton scranton pennsylvania secretary secure security seek select senate senate pass senate republicans send send best send prayers serve service service country set share shoot shoot success short shut shut virus sick sick struggle sicker sicker poorer sign sign law silent simple simple folks simple idea simple truth simply single single american single day single thing slow slow coronavirus slower small small businesses smart smart tough social social security society solidarity solve son soon soul soul nation spend spirit spread squander staff stage stake stand stand together start state state america state country stay step step job stoke stoke flame stop stop try strategy street strength strengthen strive strong stronger stronger trial struggle struggle worry success success mar suggest super super wealthy support support mask support need supporters supremacist supremacists supremacy supreme supreme court sure sure american surge surge nation swift swift recovery system systemic systemic racism tackle tackle climate target tax tax return tear tear apart tee tee round tell tell bob tell differently tell truth tens tens millions term terror test test accelerate test strategy text thank thing thing american thing vice things things build things crises think thoughts threat threat covid threats threshold throw throw obamacare time time again time come time reward time step time time time tough time virus today today donald today honor today house today let today mark today senate today vote toe together together america together emerge together experts together nation together overcome toll tone tonight tooth tooth nail tough trace track track president track worst tragedy transform transform nation treat treat dignity treatments treatments vaccines trial trial tribulation tribulation trip true truer truer today truly trump trump administration trump america trump callously trump care trump claim trump continue trump covid trump downplay trump fail trump failure trump job trump most trump need trump plan trump promise trump republicans trump responsible trump say trump simply trump tell trump think trump try trump want trump wealthy trump white trump world trump worst trump years trust truth truth covid truth donald truth economy truth listen try try gut try throw tune turbulent turbulent threshold turkey turn turn away tweet ultimate unable unacceptable unacceptable president unbelievable unconscionable understand unemployment unemployment benefit unemployment claim unfit unfit lead unforgivable union union strive unions unite unite country unite divide unite state unity unity division unjustifiable unlike urge urgency urgent use use incite use say use states use troll usher usher new utterly utterly disqualify vaccine vaccine product vaccine save vaccine stake vaccines vaccines ready vaccines waste value value learn value more value oval veday veday america vegas vegas strike venezuela venezuela pray venezuelan venezuelan independence venezuelan people venezuelans venezuelans continue venture venture capital very vice vice president view wear violence violence america violence donald violence epidemic violence generosity violence stoke violent virus virus control virus country virus covid virus enough virus fade virus february virus health virus wave voice voice hear vote vote against vote deserve vote donald vote early vote mail vote person vote right voter voter registration voters vulnerable wage wage end wage overtime wait wake wall wall bridge wall open wall president wall street want want ballot want billion want cheerleader want clarify want country want declare want destroy want downplay want extend want fact want fair want future want grow want know want member want nation want oversight want pollute want president want protect want rich want safe want single want stake want style want super want surge want tell want thing want wish war war america war build war engine war local war place war secure warm warm celebration warn warn health wartime wartime president wash wash hand washington washington america washington martin waste waste exactly waste time watch watch greatest watch kamalaharris watch leadership watch live watch sign watch years water water drink wave wave justice wave white way way cdc way close way donald way dream way mean way steady way stuff way things weak weaker weaker sicker wealth wealthy wealthy big wealthy choice wealthy friends wealthy well weapons weapons high weapons war wear wear mask week weekend weeks weeks today well well connected whistle white white flag white house white supremacist white supremacists white supremacy win win thing wish wish celebrate women wonder woodward word word possibilities word president work work day work equal work families work folks 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In [86]:
#Call the logistic regression model on biden data to predict the sentimnets of his tweets and store them on the predicted
#sentiment column
bidendf['predicted_sentiment'] = log_reg.predict(d2)
#Display the first 6 rows
bidendf.head(6)
Out[86]:
Date cleanTweets cleanTweets_count predicted_sentiment
0 11/2/2020 donald,trump,most,corrupt,president,modern,history,donald,trump,most,racist,president,modern,history,donald,trump,worst,job,president,modern,history,years 22 1
1 11/2/2020 truth,donald,trump,inherit,grow,economy,president,obama,like,inherit,life,squander 12 1
2 11/2/2020 america,vote,america,hear,america,hear,believe,message,loud,clear,time,donald,trump,leave,white,house 16 1
3 11/2/2020 biden,harris,administration,implement,nationwide,mask,mandate,ensure,access,regular,reliable,free,test,accelerate,development,distribution,safe,effective,treatments,vaccines,waste,time,virus,control 24 0
4 11/2/2020 together,rebuild,economy,build,build,better 6 0
5 11/2/2020 public,president,trump,compare,covid,flu,suggest,people,inject,bleach,treat,private,tell,bob,woodward,deadlier,flu,want,downplay,unthinkable 20 1
In [87]:
#Decoding the binary labels to Positive and Negative sentiment label for the visualization purpose 
decode_map = {0: "Negative", 1: "Positive"}
def sentiment_decode(label):
    return decode_map[int(label)]
bidendf.predicted_sentiment = bidendf.predicted_sentiment .apply(lambda x: sentiment_decode(x))
bidendf.head(3)
Out[87]:
Date cleanTweets cleanTweets_count predicted_sentiment
0 11/2/2020 donald,trump,most,corrupt,president,modern,history,donald,trump,most,racist,president,modern,history,donald,trump,worst,job,president,modern,history,years 22 Positive
1 11/2/2020 truth,donald,trump,inherit,grow,economy,president,obama,like,inherit,life,squander 12 Positive
2 11/2/2020 america,vote,america,hear,america,hear,believe,message,loud,clear,time,donald,trump,leave,white,house 16 Positive
In [88]:
#Print the percentage of positive and negative tweets of Donald Trump
bPositive_sents= round(len(bidendf[bidendf['predicted_sentiment']== "Positive"])*100/len(bidendf['predicted_sentiment']),2)
bNegative_sents= 100-bPositive_sents

print("Biden Positive Sentiment percentage is:")
print(bPositive_sents , "%")    
print("Biden Negative Sentiment percentage is:")
print(bNegative_sents , "%")
Biden Positive Sentiment percentage is:
63.15 %
Biden Negative Sentiment percentage is:
36.85 %

Visualizations

In [89]:
#Create column called month on biden dataset based on tweets date
import datetime
bidendf['month'] = pd.DatetimeIndex(bidendf['Date']).month
In [90]:
# Decode months to get months name for visualization
decode_map = {5: "May", 6: "June", 7: "July", 8: "August", 9: "September", 10: "October", 11: "November"}
def Month_decode(label):
    return decode_map[int(label)]
bidendf.month = bidendf.month.apply(lambda x: Month_decode(x))
In [91]:
# Get the count of positive and negative tweets of Biden in each month; Here we Group by month
biden_sentiment = bidendf.groupby(['month', 'predicted_sentiment']).predicted_sentiment.count().unstack()
In [92]:
#Calculate the number of tweets per month and percentage of positive and negative tweets on each month and create columns associated to them
biden_sentiment['Total_sentiments'] = biden_sentiment['Negative'] + biden_sentiment['Positive']
biden_sentiment['Biden_PositiveSent_Percentage'] = round(100*biden_sentiment['Positive']/biden_sentiment['Total_sentiments'],2)
biden_sentiment['Biden_NegativeSent_Percentage'] =round(100*biden_sentiment['Negative']/biden_sentiment['Total_sentiments'],2)
In [93]:
#Drop Columns that we do not need
biden_sentiment1 = biden_sentiment.drop(["Negative","Positive", "Total_sentiments"],axis = 1)
#Display part of the data
biden_sentiment1.head(5)
Out[93]:
predicted_sentiment Biden_PositiveSent_Percentage Biden_NegativeSent_Percentage
month
August 64.08 35.92
July 52.70 47.30
June 74.03 25.97
May 50.91 49.09
November 55.88 44.12
In [94]:
#We create a dataframe from above for visualization 
data = {'month':['May', 'June', 'July', 'August', 'September', 'October', 'November'], 
        'Biden_PositiveSent_Percentage':[50.91,74.03,52.70,64.08,64.56,67.31,55.88], 
        'Biden_NegativeSent_Percentage':[49.09,25.97,47.30,35.92,35.44,32.62,44.12]}
biden_monthly=pd.DataFrame(data, columns =['month', 'Biden_PositiveSent_Percentage','Biden_NegativeSent_Percentage'])
print(biden_monthly)
       month  Biden_PositiveSent_Percentage  Biden_NegativeSent_Percentage
0        May                          50.91                          49.09
1       June                          74.03                          25.97
2       July                          52.70                          47.30
3     August                          64.08                          35.92
4  September                          64.56                          35.44
5    October                          67.31                          32.62
6   November                          55.88                          44.12
In [95]:
#Plot biden tweets sentiment ratio per each month (From May to November 2nd)
import plotly.graph_objects as go

Months = ['May', 'June', 'July', 'August', 'September', 'October', 'November']
lis_pos_percent = [50.91,74.03,52.70,64.08,64.56,67.31,55.88]
lis_neg_percent = [49.09,25.97,47.30,35.92,35.44,32.62,44.12]

fig1 = go.Figure(data=[
    go.Bar(name='Positive', x=Months, y=lis_pos_percent , marker_color = 'teal'),
    go.Bar(name='Negative', x=Months, y=lis_neg_percent, marker_color = 'salmon') 
])
# Change the bar mode
fig1.update_layout(barmode='group', title = "Biden Tweets Sentiment Ratio Per each Month", xaxis_title= "Month", 
                  yaxis_title = "Sentiment Percentage", legend_title = "Sentiment")
fig1.show()

Here we will do the same process for trump dataset

In [96]:
#Create column called month on trump dataset based on tweets date
trumpdf['month'] = pd.DatetimeIndex(trumpdf['Date']).month
In [97]:
# Decode months to get months name for visualization
decode_map = {5: "May", 6: "June", 7: "July", 8: "August", 9: "September", 10: "October", 11: "November"}
def Month_decode(label):
    return decode_map[int(label)]
trumpdf.month = trumpdf.month.apply(lambda x: Month_decode(x))
In [98]:
# Get the count of positive and negative tweets of trump in each month; Here we Group by month
trump_sentiment = trumpdf.groupby(['month', 'predicted_sentiment']).predicted_sentiment.count().unstack()
In [99]:
#Calculate the number of tweets per month and percentage of positive and negative tweets on each month and create columns associated to them
trump_sentiment['Total_sentiments'] = trump_sentiment['Negative'] + trump_sentiment['Positive']
trump_sentiment['Trump_PositiveSent_Percentage'] = round(100*trump_sentiment['Positive']/trump_sentiment['Total_sentiments'],2)
trump_sentiment['Trump_NegativeSent_Percentage'] =round(100*trump_sentiment['Negative']/trump_sentiment['Total_sentiments'],2)
In [100]:
#Drop Columns that we do not need
trump_sentiment1 = trump_sentiment.drop(["Negative","Positive", "Total_sentiments"],axis = 1)
#Display part of the data
trump_sentiment1.head(7)
Out[100]:
predicted_sentiment Trump_PositiveSent_Percentage Trump_NegativeSent_Percentage
month
August 55.61 44.39
July 57.67 42.33
June 49.06 50.94
May 50.52 49.48
November 53.85 46.15
October 55.56 44.44
September 52.11 47.89
In [101]:
#We create a dataframe from above for visualization 
data = {'month':['May', 'June', 'July', 'August', 'September', 'October', 'November'], 
        'Trump_PositiveSent_Percentage':[50.52,49.06,57.67,55.61,52.11,55.56,53.85], 
        'Trump_NegativeSent_Percentage':[49.48,50.94,42.33,44.39,47.89,44.44,46.15]}
trump_monthly=pd.DataFrame(data, columns =['month', 'Trump_PositiveSent_Percentage','Trump_NegativeSent_Percentage'])
print(trump_monthly)
       month  Trump_PositiveSent_Percentage  Trump_NegativeSent_Percentage
0        May                          50.52                          49.48
1       June                          49.06                          50.94
2       July                          57.67                          42.33
3     August                          55.61                          44.39
4  September                          52.11                          47.89
5    October                          55.56                          44.44
6   November                          53.85                          46.15
In [102]:
#Plot trump tweets sentiment ratio per each month (From May to November 2nd)

Months = ['May', 'June', 'July', 'August', 'September', 'October', 'November']
lis_pos_percent = [50.52,49.06,57.67,55.61,52.11,55.56,53.85]
lis_neg_percent = [49.48,50.94,42.33,44.39,47.89,44.44,46.15]

fig2 = go.Figure(data=[
    go.Bar(name='Positive', x=Months, y=lis_pos_percent, marker_color = 'teal'),
    go.Bar(name='Negative', x=Months, y=lis_neg_percent, marker_color = 'salmon')
])
# Change the bar mode
fig2.update_layout(barmode='group', title = "Trump Tweets Sentiment Ratio Per each Month", xaxis_title= "Month", 
                  yaxis_title = "Sentiment Percentage", legend_title = "Sentiment")
fig2.show()
In [103]:
#Overal Sentiment results for both candidates for comparison purpose 
Politicians = ['Donald Trump', 'Joe Biden']
lis_pos = [tPositive_sents, bPositive_sents]
lis_neg = [tNegative_sents, bNegative_sents]

fig3 = go.Figure(data=[
    go.Bar(name='Positive', x=Politicians, y=lis_pos, marker_color = 'teal' ),
    go.Bar(name='Negative', x=Politicians, y=lis_neg,  marker_color = 'salmon')
])
# Change the bar mode
fig3.update_layout(barmode='group', title = "Candidates Overal tweets Sentiment comparison", xaxis_title= "Presidential Candidates", 
                  yaxis_title = "Sentiment Percentage", legend_title = "Sentiment")
fig3.show()
In [104]:
#Merge trump and biden monthly sentiments in one dataframe for visulization 
trump_biden_merged = pd.merge(biden_monthly, trump_monthly, on = 'month')
In [105]:
#Keep only positive sentiments for both candidate for comparison of their positive tweets change over the defined time period
trump_biden_positiveSents = trump_biden_merged.drop("Biden_NegativeSent_Percentage",axis=1)
trump_biden_positiveSents= trump_biden_positiveSents.drop("Trump_NegativeSent_Percentage", axis=1)
print(trump_biden_positiveSents)
       month  Biden_PositiveSent_Percentage  Trump_PositiveSent_Percentage
0        May                          50.91                          50.52
1       June                          74.03                          49.06
2       July                          52.70                          57.67
3     August                          64.08                          55.61
4  September                          64.56                          52.11
5    October                          67.31                          55.56
6   November                          55.88                          53.85
In [106]:
# Plot the candidate positive sentiments tweets change over time
ax = plt.gca()
trump_biden_positiveSents.plot(kind='line',x='month',y='Biden_PositiveSent_Percentage', color='blue', ax=ax,  figsize=(10,4))
trump_biden_positiveSents.plot(kind='line',x='month',y='Trump_PositiveSent_Percentage', color='red', ax=ax , figsize=(10,4))
ax.set_ylim(ymin=40)
plt.title('Presidential Candidates Positive Sentiment Changes Up to the Election Day')
plt.show()
C:\Users\ellie\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py:1235: UserWarning:

FixedFormatter should only be used together with FixedLocator